Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based...

105
Behavior Control Cognitive Robotics Rijeka 2018

Transcript of Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based...

Page 1: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Behavior Control

Cognitive Robotics

Rijeka 2018

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 2

Behavior Control needs hellip hellip integration of perception decisionplanning action on different complexity levels All parts depend on the others Improving one part may result in worse performance

Burkhard Cognitive Robotics Behavior Control 3

bull Household much more complicated than car driving bull Soccer much more complicated than chess

Programming Environments Different tools for bull Development of Programs bull Checking Programs bull Middleware Next Slides RobotControl developped for GermanTeam (Aibos)

Burkhard Cognitive Robotics Behavior Control 4

Widely used ROS (= Robot Operating System) httpwikirosorg

Burkhard 5 Cognitive Robotics Behavior Control

Burkhard 6 Cognitive Robotics Behavior Control

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 2: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 2

Behavior Control needs hellip hellip integration of perception decisionplanning action on different complexity levels All parts depend on the others Improving one part may result in worse performance

Burkhard Cognitive Robotics Behavior Control 3

bull Household much more complicated than car driving bull Soccer much more complicated than chess

Programming Environments Different tools for bull Development of Programs bull Checking Programs bull Middleware Next Slides RobotControl developped for GermanTeam (Aibos)

Burkhard Cognitive Robotics Behavior Control 4

Widely used ROS (= Robot Operating System) httpwikirosorg

Burkhard 5 Cognitive Robotics Behavior Control

Burkhard 6 Cognitive Robotics Behavior Control

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 3: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Behavior Control needs hellip hellip integration of perception decisionplanning action on different complexity levels All parts depend on the others Improving one part may result in worse performance

Burkhard Cognitive Robotics Behavior Control 3

bull Household much more complicated than car driving bull Soccer much more complicated than chess

Programming Environments Different tools for bull Development of Programs bull Checking Programs bull Middleware Next Slides RobotControl developped for GermanTeam (Aibos)

Burkhard Cognitive Robotics Behavior Control 4

Widely used ROS (= Robot Operating System) httpwikirosorg

Burkhard 5 Cognitive Robotics Behavior Control

Burkhard 6 Cognitive Robotics Behavior Control

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 4: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Programming Environments Different tools for bull Development of Programs bull Checking Programs bull Middleware Next Slides RobotControl developped for GermanTeam (Aibos)

Burkhard Cognitive Robotics Behavior Control 4

Widely used ROS (= Robot Operating System) httpwikirosorg

Burkhard 5 Cognitive Robotics Behavior Control

Burkhard 6 Cognitive Robotics Behavior Control

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 5: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard 5 Cognitive Robotics Behavior Control

Burkhard 6 Cognitive Robotics Behavior Control

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 6: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard 6 Cognitive Robotics Behavior Control

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 7: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard 7

Simulator Red robot in the middle Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 8: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

8

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 9: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Where to intercept the ball

Burkhard Cognitive Robotics Behavior Control 9

By calculation By simulation By learned behavior

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 10: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Which player can intercept first Based on calculation of intercept point

Burkhard Cognitive Robotics Behavior Control 10

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 11: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Pass to which team mate

Burkhard Cognitive Robotics Behavior Control 11

Based on calculations of intercepting players

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 12: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Simulation 2D RoboCup1997 Nagoya Final Match AT-Humboldt (Humboldt University of Berlin Germany) vs andhill (Tokyo Institute of Technology Japan)

Burkhard Cognitive Robotics Behavior Control 12

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 13: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

Chess like Control for Soccer Evaluate options for future success

Choose the best alternative

13

Does not work for more Complicated situations

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 14: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 14

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 15: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Classical Types of AgentRobot Behavior Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 15

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 16: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

Reactive Behavior

environment sensors effectors

Immediate Action

agent

Perception

52 16

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 17: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 17

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 18: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 18

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 19: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 19

Run for the ball

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 20: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Reactive (ldquostimulus-responserdquo)

Burkhard Cognitive Robotics Behavior Control 20

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 21: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

Goal directed behavior

environment sensors

Individual Plan

effectors

Individual Planner

Immediate Reaction

agent

Perception Action

57

Worldmodel

21

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 22: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 22

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 23: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 23

Acting according to a predefined goal

x

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 24: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Goal directed behavior

Burkhard Cognitive Robotics Behavior Control 24

Acting according to a predefined goal

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 25: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

ldquoMental Statesrdquo Past Belief (world model) Future Commitment (goal intention plan )

sensors

Commitments

effectors

Individual Planner

Immediate Reaction

agent

Belief

Perception Action

x

60 25

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 26: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

Cooperative Behavior

environment sensors effectors

Cooperative Planner

Individual Planner

Immediate Reaction

agent

Perception Action

63

Belief

Individual Commitments

Joint Commitments

26

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 27: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 27

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 28: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 28

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 29: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 29

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 30: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 30

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 31: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 31

Cooperation Joint intention (Double pass)

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 32: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Cooperative Behavior

Burkhard Cognitive Robotics Behavior Control 32

Cooperation Joint intention (Double pass)

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 33: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Control Architectures Distribution Interfaces Information flow

Burkhard Cognitive Robotics Behavior Control 33

Sensors Actuators

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 34: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

bdquoHorizontalldquo Structure

Burkhard Cognitive Robotics Behavior Control 34

Actuators

Sense Think Act

Sensors

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 35: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Synchronisation

Burkhard Cognitive Robotics Behavior Control 35

sense think act

sense think act

sense think act conflict

Sequential

Parallel

Real Time Requirements may lead to conflicts

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 36: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Different Complexities

Burkhard Cognitive Robotics Behavior Control 36

bull World Model

bull

bull Simple Percepts

bull Sensor Signals

bull Cooperative Planning

bull Planning

bull

bull Choice of Skill

bull Reaction (Stimulus Response)

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 37: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Layered Architecture Example

Burkhard Cognitive Robotics Behavior Control 37

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Deliberative Layer Long Term Planning Time consuming

Working Layer

Scheduling of bdquoskillsldquo Needs moderate time

Reactive Layer

Immediate reactions

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 38: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Layered Architecture Reaction Time

Burkhard Cognitive Robotics Behavior Control 38

Actuators

Sense Think Act

Sensors

Sense Think Act

Sense Think Act

Different cycle times at the layers

Time problems with ldquoupwards failuresrdquo 1 Problem on low level 2 Higher levels react with delay ldquomoment of schockrdquo

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 39: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Upwards Failure Planned behavior (double pass) Intercept fails but other player continues

Burkhard Cognitive Robotics Behavior Control 39

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 40: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

How to Organize Data Flow

Burkhard Cognitive Robotics Behavior Control 40

Sense Think Act

Sensors Actuators

Signals

WorldModel

Response

Planning

Action

Plan

Need for reduction of dependencies

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 41: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Classical One-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 41

Sense Think Act

Sensors Actuators

Worldmodel

Beliefs

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 42: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 42

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Low-level Controller

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 43: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Classical Two-Pass-Architecture

Burkhard Cognitive Robotics Behavior Control 43

Sense Think Act

Sensors Actuators

Worldmodel

Knowledge Base

Pilot

Navigator

Mission Planner

Low-level Contoller

Example 3-Tiered (3T) Architecture (NASA)

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 44: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 44

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 45: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard

Concept (Bounded) Rationality Rational Choice Assumption bull Agents act as utility maximizers Critics (Simon) Bounded Rationality bull Only limited knowledge about real world available bull Only limited resources for deliberation

Needs exact knowledge about future consequences

Cognitive Robotics Behavior Control 45

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 46: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Ideal Rational Agents

Aspects of the definition bull Performance measure determines the purposes of agentrobot bull Design problem designer has to built the necessary means

Critical assumption Real agents are not ldquoidealrdquo rational agents

Burkhard Cognitive Robotics Behavior Control 46

Definition by RussellNorvig Artificial Intelligence ndash A Modern Approach

For each possible percept sequence an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 47: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Limitations of Rational Agents Limitations of ldquoidealrdquo agent bull ldquoevidence provided by the percept sequencerdquo limited by partial and incomplete information (belief) bull ldquowhatever built-in knowledgerdquo limited by insufficient internal models about the world Further limitations of ldquorealrdquo agent bull ldquomaximize its performance measurerdquo limited by available resources

bull hardwaresoftware bull time constraints

Burkhard 47 Cognitive Robotics Behavior Control

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 48: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Bounded Rationality

Burkhard Cognitive Robotics Behavior Control 48

Real agents are neither ldquoidealrdquo nor completely ldquorationalrdquo agents Compromises by design bull Dealing with trade-offs bull Do ldquoas best as canrdquo

ldquoBounded Rationalityrdquo Efficiency wrt limited resources

Notion from economics introduced by Herbert Alexander Simon 1955 (further aspect Irrationalities of decisions)

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 49: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 49

Player at time t

Ball observed at time t

Timepoint t

Intercept expected at time t+9

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 50: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 50

Player at time t

Timepoint t+3

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+9

Ball observed at time t

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 51: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 51

Player at time t

Timepoint t+6

Player at time t+3

Ball observed at time t+3

Intercept expected at time t+11

Ball observed at time t

Player at time t+6

Ball observed at time t+6

Time lost by changing directions

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 52: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

Conflicts between oldnew Options Keep old option

+ Stability + Reliabilty (cooperation) - Fanatism (misses better options)

Change for new option

+ Adapt to better options - May lead to oscillations

Treatment of conflict is up to choice by designer (architecture may even cause an implicit design decision)

52

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 53: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Protocols for Coordination Coordination possible without communication hellip hellip if all robots have the same world model and follow the same protocol Otherwise communication can help to bull unify world model bull distribute rolestasks (by protocol or negotiation or leader hellip)

Burkhard Cognitive Robotics Behavior Control 53

Communication - needs time - can be disrupted - can be inconsistentconflicting

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 54: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Example Roles by Protocol

Burkhard Cognitive Robotics Behavior Control 54

Role Task Assignment by Goalie defend goal fixed Attacker ball handling closest to ball Supporter support attacker close to attacker Defender backward support most back

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 55: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Example Stability vs Adaptation

Burkhard Cognitive Robotics Behavior Control 55

Solution Keep role in case of small deviations (ldquoHysteresis controlrdquo)

Role change Player closest to ball is attacker Distance to ball can oscillate by noisy observations

Difference of distances to ball

attacker

supporter

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 56: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Competing Desires Robot wants eg to - Change position (for supporting) - Avoid obstacles - Look for landmarks (for localization) - Observe the ball - Observe other players Can he pursue all these desires in parallel Rational behavior Commit only to achievable intentions

Burkhard Cognitive Robotics Behavior Control 56

Possible solution ldquoScreen of admissibilityrdquo Adapt new intentions only if not in conflict with already

adapted intentions (reg gives priority to stability)

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 57: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Least Commitment Cannot plan all future details in advance

Burkhard Cognitive Robotics Behavior Control 57

Solution ldquoLeast commitmentrdquo Postpone decisions as long as possible

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 58: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 58

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 59: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

BDI Agent Architecture Most popular architecture for reasoning agents Originally based on concepts of Michael E Bratman ldquoIntention Plans and Practical Reasonrdquo 1987 The architecture is built on - Possible facts about the world - Potential options the agent might achieve BDI stands for Beliefs Information the agent has about the world Desires States of affairs the agent would like to accomplish Intentions States of affairs the agent is trying to accomplish

Burkhard 59 Cognitive Robotics Behavior Control

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 60: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control

BDI-Modell Belief (world modell) Desire (useful options) Intention (committed options)

perceive Belief

select Desire

means-ends Intention

execute sense

new_Belief = update(Perception old_Belief) new_Desires = select (new_Beliefold_Desires) new_Intentions = means-ends(new_Belief new_Desires _old_Intentions)

61 60

Consistency ndashDesires may be inconsistent ndashIntentions must be consistent

Intentions set a screen of admissibility Only those desires may be adopted which are consistent with recent intentions

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 61: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

AgentSpeak and Jason AgentSpeak is a logic-based agent-oriented programming language implementing (some) concepts of BDI-architectures proposed by Arnand S Rao 1996 based on experiences with

PRS (Georgeff Lansky 1987) dMARS (Kinny 1993) Agent-0 (Shoham 1993)

Jason extension of AgentSpeak with Prolog-like syntactic structures interpreter in Java highly customizable developped by Rafael H Bordini Jomi F Huumlbner and others

Burkhard 61 Cognitive Robotics Behavior Control

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 62: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Interpreter

Works with Plan Library (initially filled) Belief Base (Memory of actual beliefs) Event Base (Memory for changes of beliefs and goals) Intention Base (Stacks of pending goals) Selection functions to select from the different Bases

Burkhard Cognitive Robotics Behavior Control 62

Next Slides Syntax and Informal Semantics Cited from BordiniHubner Jason - A Java-based interpreter for an extended version of AgentSpeak Release Version 095 February 2007 httpjasonsourceforgenetJasonpdf

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 63: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Beliefs in Jason

Beliefs first-order formulae ball(10 10) agent believes the ball is at position (10 10)

Beliefs can have annotations ball(10 10)[source(percept)] information was perceived from environment

Support for strong negation (besides negation by CWA) ~near(ball) agent believes itrsquos not near the ball

Belief base can also process (Prolog-like) rules

Burkhard 63 Cognitive Robotics Behavior Control

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 64: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Goals

2 types of goals Achievement goals for calling plans

kick(ball) might eg invoke a plan to bend the knee and kick

Test goals for tests of beliefs see(ball) succeeds if the agent actually sees the ball

Burkhard 64 Cognitive Robotics Behavior Control

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 65: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Plans

Plan in rule form triggeringEvent (Change of beliefs goals hellip) context (Conditions which must hold) lt- body (Sequence of goals and external actions)

Burkhard 65 Cognitive Robotics Behavior Control

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 66: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Simplified Reasoning Cycle

1 Update belief base by external percepts 2 Update event base according to previous steps 3 Select actual triggering event e 4 Determine relevant plans by unification with e 5 Determine applicable plans by checking contexts 6 Select a plan p from applicable plans 7 Update actually processed intention according to p resp initialize new intention (for external event) 8 Select intention i for processing 9 Execute next subgoal from top of intention i perform external action or update belief or test belief

Burkhard 66 Cognitive Robotics Behavior Control

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 67: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Implementation of Soccer Agents

67 Burkhard Cognitive Robotics Behavior Control

Perceptors Effectors Control (ldquoAgentrdquo)

RoboCup Simspark

Player-Program

Player

each 20 msec bullJoint angles bullAcceleration bullMicrophon bullcamera) hellip

Motor commands Loudspeaker hellip

) Camera percepts only each 60 msec ldquo

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 68: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Implementation of Soccer Agents Sense-think-act-cycle Sense process perceptor data from SimSpark Simulator implemented in Java Think analyse sitation and specify goals implemented in Jason Act send action commands to SimSpark Simulator implemented in Java

Burkhard Cognitive Robotics Behavior Control 68

Experimental Implementation of Soccer Agents by Dejan Mitrovic (Novi Sad)

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 69: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Example of a Simple Agent

Burkhard 69 Cognitive Robotics Behavior Control

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 70: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

(Partial) Implementation of the Example

Burkhard 70 Cognitive Robotics Behavior Control

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 71: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

DPA = Double Pass Architecture hellip another approach to implement BDI

Burkhard Cognitive Robotics Behavior Control 71

Diploma Thesis Ralf Berger used in RoboCup 2D league

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 72: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 72

1 Trial (bdquoChess-likeldquo)

bull Foresight simulation

bull Choice of best alternative

Result Useful only for short term decisions

2 Trial (bdquoEmergenceldquo)

If every player behaves in some simple optimal way

then a double pass can emerge without planning Result Double pass emerges only from time to time

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 73: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

How to program a double pass

Burkhard Cognitive Robotics Behavior Control 73

3 Trial

bull Use Bratmanacutes concept of bdquobounded rationalityldquo

Belief-Desire-Intention-Architecture (BDI)

bull Use Case-Based Reasoning

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 74: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Only partial plan in the beginning

Burkhard Cognitive Robotics Behavior Control 74

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 75: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute10acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 75

Analysis of situation ltsituation descriptiongt

Dribbling on path ltgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 76: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute11acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 76

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt to team mate 10

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute10acuteacute

Dribbling on path ltgt 5acute11acuteacute

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 77: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute12acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 77

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltgt

5acute10acuteacute

5acute12acuteacute

Run on path ltgt over opponent 7

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 78: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute13acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 78

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 79: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute14acuteacute(Least Commitment)

Burkhard Cognitive Robotics Behavior Control 79

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute14acuteacute Run on path ltgt

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 80: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute15acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 80

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

Run to ball on path ltgt kicked by team mate 10

5acute15acuteacute Run on path ltgt

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 81: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute16acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 81

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute16acuteacute Run on path ltgt

Run to ball on path ltgt kicked by team mate 10

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 82: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute17acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 82

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

Intercept ball at point ltgt optimal intercept point

5acute17acuteacute Run to ball on path ltgt

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 83: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute18acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 83

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute 5acute18acuteacute

Intercept ball at point ltgt optimal intercept point Run to ball on path ltgt

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 84: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute19acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 84

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltgt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 85: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Time 5acute20acuteacute (Least Commitment)

Burkhard Cognitive Robotics Behavior Control 85

Analysis of situation ltsituation descriptiongt

Dribbling on path ltpath parametersgt

Kick with parameters ltball speed vectorgt

Run on path ltpath parametersgt

Intercept ball at point ltpositiongt

Run to ball on path ltpath parametersgt

5acute10acuteacute

5acute12acuteacute 5acute13acuteacute

5acute17acuteacute

5acute19acuteacute 5acute20acuteacute

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 86: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Hierarchy of Options

Burkhard Cognitive Robotics Behavior Control 86

PlaySoccer

Offensive Defensive

Score OffsideTraAttack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 87: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Result of ldquoDeliberatorldquo Intention Subtree

Burkhard Cognitive Robotics Behavior Control 87

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 88: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 88

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 89: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 89

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

bdquoExecutorldquo checks conditions on all levels of the hierarchy and decides by least commitment principle

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 90: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 90

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 91: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 91

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 92: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 92

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 93: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 93

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 94: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Activity Path Present state of an Intention

Burkhard Cognitive Robotics Behavior Control 94

PlaySoccer

Offensive Defensive

Score OffsideTrap Attack ChangeWings1 DoublePass2 DoublePass1

Dribble

Pass

Intercept

Run

Kick

Reposition

Pass ready next Run

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 95: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Double-Pass Architecture bull Predefined Option Hierarchy bull Deliberator bull Executor

bdquoDoubledldquo 1-Pass-Architecture

1 Pass Deliberator (goal-oriented intention subtree) 2 Pass Executor (stimulus-response activity path)

- on all levels - Differences to ldquoclassicalrdquo Programming

Control flow by Deliberation (ldquoAgent- orientedrdquo) Runtime organization by 2 Passes through all levels

Burkhard Cognitive Robotics Behavior Control 95

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 96: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Overview Introduction Control Architectures Aspects of Rationality BDI Architectures Behavior Based Robotics

Burkhard Cognitive Robotics Behavior Control 96

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 97: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 97

Behavior Based Robotics Simple behavior by eg - Immediate reaction to sensor data (sensor-actor-coupling) - Simple physical bdquotransformationldquo (clever design) Intelligent action without intelligent thinking bull No worldmodel bull No symbols bull No deliberation

Hypothesis Complex behavior emerges by combination of simple behaviors

Emergent behavior Complex behavior emerges by interaction of situated robots with the environment

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 98: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 98

bdquoNew AIldquo Since middle of 1980s Orientation on natural principles - Emergent behavior - Situated agentsrobots - No internal representation

Papers by Rodney Brooks bdquoElefants donacutet play chessldquo bdquoIntelligence without reasonldquo bdquoIntelligence without representationldquo Patty Maes

Kismet

Coq Roomba

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 99: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Critics on Classical AI GOFAI = ldquoGood Old Fashioned AIrdquo Problems with bull Closed world assumption bdquoeverything is knownldquo bull Frame problembdquoall assumptionseffects are modelled bull Physical systems hypothesis complete symbolic

representation

Burkhard Cognitive Robotics Behavior Control 99

1966-72 Robot Shakey (Stanford) with hierarchical planner STRIPS (bdquoStanford Research Institute Problem Solverldquo)

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 100: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 100

Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means for intelligent actionldquo

NewellSimon Computer Science as Empirical Inquiry Symbols and Searchldquo

Many critics (Dreyfus Searle Penrose Brooks Maes Pfeiffer)

Needs bull Complete Descriptions of the Worlds bull Algorithms for actions

GOFAI= bdquogood old fashioned AIldquo

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 101: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 101

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 102: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 102

Physical Grounding Hypothesis This hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in the physical world Our experience with this approach is that once this commitment is made the need for traditional symbolic representations fades entirely The key observation is that the world is its own best model It is always exactly up to date It always contains every detail there is to be known The trick is to sense it appropriately and often enough

To build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of interest They are not physically grounded RA Brooks Elephants Donacutet Play Chess

New Problem

But To bring the Beer from the basement the robot should have an idea about the location etc

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 103: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 103

Subsumption Architecture (Brooks) Example

Sense Act

Sensors Actuators

Avoid obstacles

Drive forward

Collect can

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 104: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Burkhard Cognitive Robotics Behavior Control 104

Subsumption Architecture (Brooks) bull Behaviors realized by simple AFSM (augmented finite state machines) bull No other internal modelling bull Layers Hierarchical collection of behaviors bull Parallel control by all layers bull In case of conflicts higher layer overwrights (bdquosubsumesldquo) other layers

First successful robot designs for simple tasks

Problems with too many behaviors Design and prediction of resulting behavior

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed
Page 105: Behavior Controlnaoth/RoboNewbie/...Most popular architecture for reasoning agents. Originally based on concepts of Michael E. Bratman: “Intention, Plans, and Practical Reason”

Consequence Different Approaches Needed Reactive Behavior

like Stimulus-Response short term bdquosimpleldquo behavior patterns simple skills

Deliberative Behavior Goal directed plan based behavior long term bdquocomplexldquo behavior

Hybrid Combination of reactive and deliberative behavior eg goal driven usage of reactive skills

Burkhard Cognitive Robotics Behavior Control 105

In robotics up to now More emphasis put to aspects of low level control Recently Increasing interest in high level control

  • Behavior Control
  • Overview
  • Behavior Control needs hellip
  • Programming Environments
  • Foliennummer 5
  • Foliennummer 6
  • Foliennummer 7
  • Chess like Control for Soccer
  • Where to intercept the ball
  • Which player can intercept first
  • Pass to which team mate
  • Simulation 2D
  • Chess like Control for Soccer
  • Overview
  • Classical Types of AgentRobot Behavior
  • Reactive Behavior
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Reactive (ldquostimulus-responserdquo)
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • Goal directed behavior
  • ldquoMental Statesrdquo
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Cooperative Behavior
  • Control Architectures
  • bdquoHorizontalldquo Structure
  • Synchronisation
  • Different Complexities
  • Layered Architecture Example
  • Layered Architecture Reaction Time
  • Upwards Failure
  • How to Organize Data Flow
  • Classical One-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Classical Two-Pass-Architecture
  • Overview
  • Concept (Bounded) Rationality
  • Ideal Rational Agents
  • Limitations of Rational Agents
  • Bounded Rationality
  • Example Stability vs Adaptation
  • Stability vs Adaptation
  • Stability vs Adaptation
  • Conflicts between oldnew Options
  • Protocols for Coordination
  • Example Roles by Protocol
  • Example Stability vs Adaptation
  • Competing Desires
  • Least Commitment
  • Overview
  • BDI Agent Architecture
  • BDI-Modell
  • AgentSpeak and Jason
  • Interpreter
  • Beliefs in Jason
  • Goals
  • Plans
  • Simplified Reasoning Cycle
  • Implementation of Soccer Agents
  • Implementation of Soccer Agents
  • Example of a Simple Agent
  • (Partial) Implementation of the Example
  • DPA = Double Pass Architecture
  • How to program a double pass
  • How to program a double pass
  • Only partial plan in the beginning
  • Time 5acute10acuteacute (Least Commitment)
  • Time 5acute11acuteacute (Least Commitment)
  • Time 5acute12acuteacute (Least Commitment)
  • Time 5acute13acuteacute (Least Commitment)
  • Time 5acute14acuteacute(Least Commitment)
  • Time 5acute15acuteacute (Least Commitment)
  • Time 5acute16acuteacute (Least Commitment)
  • Time 5acute17acuteacute (Least Commitment)
  • Time 5acute18acuteacute (Least Commitment)
  • Time 5acute19acuteacute (Least Commitment)
  • Time 5acute20acuteacute (Least Commitment)
  • Hierarchy of Options
  • Result of ldquoDeliberatorldquo Intention Subtree
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Activity Path Present state of an Intention
  • Double-Pass Architecture
  • Overview
  • Behavior Based Robotics
  • bdquoNew AIldquo
  • Critics on Classical AI
  • Physical Symbol System Hypothesis
  • Physical Grounding Hypothesis
  • Physical Grounding Hypothesis
  • Subsumption Architecture (Brooks)
  • Subsumption Architecture (Brooks)
  • Consequence Different Approaches Needed