Discussant for Last Session Bob Balzer Teknowledge Architectural Issues.

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Discussant for Last Session Bob Balzer Teknowledge Architectural Iss

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Architectural Comparison 3T World 1/10 sec. limited state limited projection memory interpreter task1 subtask task2 task3 1 sec. memory of immediate actions no projection 10s sec. persistent state and choices projection Adaptive Mission Planner Controller Synthesis Module Real Time System CIRCA Deliberation scheduling Planning Execution

Transcript of Discussant for Last Session Bob Balzer Teknowledge Architectural Issues.

Page 1: Discussant for Last Session Bob Balzer Teknowledge Architectural Issues.

Discussant for Last Session

Bob BalzerTeknowledge

Architectural Issues

Page 2: Discussant for Last Session Bob Balzer Teknowledge Architectural Issues.

Pat LangleySome motivating assumptions

• We should move beyond isolated phenomena and capabilities to develop complete intelligent systems

• AI and cognitive psychology are close allies with distinct but related goals• Characterize intelligent behavior at the level of functional structures and

processes, not at the implementation or knowledge levels• Cognitive architecture should make commitments to representations and

organizations of knowledge, the memories in which such knowledge resides and the processes – performance and learning – that operate upon them

• A cognitive architecture specifies the infrastructure that does not change over domains and time, as opposed to knowledge, which does vary

• A cognitive architecture should have an associated programming language for encoding knowledge and constructing intelligent systems

• An architecture should demonstrate generality and flexibility rather than success on a single domain or application

components, or processes, which do vary APIs

, adding components, and modifying processes

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Architectural Comparison3T

World

1/10 sec. limited state limited projection

memory interpreter

task1 subtask subtasktask2task3

1 sec. memory ofimmediate actions no projection

10s sec. persistent stateand choices projection

Adaptive Mission Planner

Controller Synthesis Module

Real Time

System

CIRCA

Deliberation scheduling

Planning

Execution

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Architecture CompositionHomogeneous Composition

Roles, Goals

Real-Time Reactions

Planned Actions,Planned Negotiations

Adaptive Mission Planner

Controller Synthesis Module

Real Time

System

Adaptive Mission Planner

Controller Synthesis Module

Real Time

System

Extending Performance Guarantees to Multi-Agent Teams

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Heterogeneous Composition

Architecture CompositionHomogeneous Composition

path planner

simulationIVHM

scheduler

Planner

Planner

Planner

T3 Multi-Agent Teams

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Architecture Composition Heterogeneous Composition

Soar

Interact with a complex world - limited uncertain sensingRespond quickly to changes in the world Use extensive knowledgeUse methods appropriate for tasksGoal-drivenMeta-level reasoning and planningGenerate human-like behaviorCoordinate behavior and communicate with others Learn from experienceIntegrate above capabilities across tasksBehavior generated with low computational expense

Target Application Behavioral Capabilities

FewProblemsMatchProfile}

CognitiveArchitecturei

CognitiveArchitecturei

CognitiveArchitecturei

Special Purpose Cognitive Architectures

ArchitectureSelector

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Architecture Composition Heterogeneous Composition

(from Jonathan Grach)

• Soar as a component in a larger architecture

Speech Recognition (HTK)

Semantic Parser

Motion/ Gesture Scheduler (Beat)

Text to Speech (Festival)

World Simulator

Animation System

BDI

Haptek

Com

mun

icat

ion

Bus

Audio (Protools)

Voice Input

Vega

Projection System

Speakers (10.2)

Soar Planning

DialogueAction Selection

Perc

eptio

n

NLG

Emotion

NLU pragmatics

Child Healthy:False

AccidentIntend: FalseBlame: unresolved

Assist Eagle 1-6:False

Eagle 1-6 AssistDesire: LT

Belief: False

Child-HealthyDesire: SGTBelief: False

Probability: 75%

Get MedevacResponsibility:LTIntend: True

Medevac Available:True

Past FuturePresent

Cognitive Representation

Soar’s Working Memory

Planning Perception Dialogue Action

Soar operators

• Defining architecture (components) within Soar– Accomplished through escape mechanism (no extension

API)

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Comparative Framework• Representational elements

– Inputs, Justified Beliefs, Assumptions, Desires, Active Goals, Plans, Actions, Outputs

• Design dimensions– Representation formalism

• How is each type of element represented?– Commitment strategy

• Under what conditions does each type of element get selected/activated/instantiated?

– Reconsideration strategy• Under what conditions does each type of element get

removed/deactivated/released?

Thinking inside the box

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Missing Elements

• Deliberate attention• Parallel active goals• Resources and limitations• Multi-agent/social elements• Learning• Episodic memory

Thinking inside the box

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Intermediate, Reusable Components

• Vocabulary for defining components• Uses: Modeling, Comparative, Generative

T h i n k i n g i n s i d e t h e b o xM a r c h 2 2 - 2 3 , 2 0 0 3 A r c h i t e c t u r e W o r k s h o p S l i d e 1 7

W o r k i n g M e m o r yE l e m e n t

B e l i e f

A s s u m p t i o n

D e s i r e

G o a l

E n t a i l m e n t

A c t i v a t e da s s e r t i o n

T h i n k i n g i n s i d e t h e b o xM a r c h 2 2 - 2 3 , 2 0 0 3 A r c h i t e c t u r e W o r k s h o p S l i d e 1 8

E n t a i l m e n t

C r e a t e : T r u t h m a i n t e n a n c e

R e l e a s e : T r u t h m a i n t e n a n c e

A s s u m p t i o n

C r e a t e : P e r s i s t e n t a s s e r t i o n

R e l e a s e : P e r s i s t e n t a s s e r t i o n

A c t i v a t e dA s s e r t i o n

C r e a t e : A c t i v a t i o n t h r e s h o l d

R e l e a s e : A c t i v a t i o n t h r e s h o l d

T e m p o r a lA s s u m p t i o n

C r e a t e : P e r s i s t e n t a s s e r t i o n

R e l e a s e : D e c a y f u n c t i o n

C o n t e x t - S e n s i t i v eA s s u m p t i o n

C r e a t e : P e r s i s t e n t a s s e r t i o n

R e l e a s e : P e r s i s t e n t a s s e r t i o nR e l e a s e : T r u t h M a i n t e n a n c e

One method for extending architectures: Libraries

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Architectural Schema

• Generative grammar for a class of architectures– Different architectures include mechanisms in different subsets of the boxes– Different possible information links, – Different possible control relationships.– Also differences in forms of representation and types of mechanisms.

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Soar ++

• How new capability was added to Soar– By modifying system (and done by system developers)

• How could such capabilities be added “externally”– What architectural extension mechanism are provided

Episodic Learning[Andrew Nuxoll]

• What is it?• Not facts or procedures but memories of specific events• Recording and recalling of experiences with the world

• Characteristics of Episodic Memory• Autobiographical• Not confused with original experience• Runs forward in time• Temporally annotated

• Why add to Soar architecture? • Not appropriate as reflective learning • Provides personal history and identity• Memories that can aid future decision making & learning• Can generalize and analyze when time and more knowledge are available

Reinforcement Learning[Shelley Nason]

• Why add it to Soar?• Might capture statistical regularities automatically/architecturally• Chunking can do this only via deliberate learning

• Why Soar?• Potential to integrate RL with complex problem solver• Quantifiers, hierarchy, …

• How can RL fit into Soar?• Learn rules that create numeric probabilistic preferences for operators• Used only when symbolic preferences are inconclusive• Decision based on all preferences that are recalled for an operator

• Why is this going to be cool?• Dynamically compute Q-values based on all rules that match state• Get transfer at different levels of generality

Architectural Extension

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Distinctive Features of EPIC Work

• Emphasis on executive processes that coordinate multitask performance– Multitask performance stresses the architecture.– An important but underdeveloped area for theory.

• Take advantage of underexploited but powerful constraints:– Perceptual-motor abilities and limitations.– Detailed and exact quantitative fits to human data.

• “Zero-based” theoretical budget:– Question traditional assumptions.– Do not add a mechanism until it is needed to account for data.– Avoid egregious assumptions of cognitive limitations.– Prefer strategy limitations over architectural ones.

• Focus on major phenomena and mechanisms that are important determinants of performance, rather than minor “interesting” ones.

• Compare multiple strategies for doing a task.– Isolate strategy effects from architectural properties.

Shouldn’t stress performance architecture

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Builds and repairs fully-detailed flight schedules for any planning horizon, without losing sight of command objectives,

providing new opportunities to explore and manage alternative futures, in 1/10th-1/100th of current time

o ConstraintsConstraintso Training code pre-requisitesTraining code pre-requisites

from T&R Manualfrom T&R Manualo Fly dayFly dayo Day & night missionsDay & night missionso Crew day rulesCrew day ruleso Turn-around & briefing timeTurn-around & briefing timeo Instructor requirementsInstructor requirementso Range capabilitiesRange capabilities

o Availability & suitabilityAvailability & suitabilityo Merging and splittingMerging and splitting

o Range boardRange boardo Pilot SNIVELsPilot SNIVELso Aircraft availabilityAircraft availabilityo Simulator schedule Simulator schedule

Range UseRange Use

Pilots’ ViewPilots’ View

Scheduling OfficerScheduling OfficerFeedbackFeedback

Status of SNAP: Schedules Negotiated by Agent-Based Planners

Identifies needed ranges

Tracks pilots

Compares results to guidance

Knows the situation

Accepts guidance at any level of specificity

Lets users adjust priorities

InputsInputs

OutputsOutputs

Prioritized GuidancePrioritized Guidanceo Squadron focusSquadron focus o Pilot focuso Pilot focus o Sortie cycleo Sortie cycleo Pilot buildsPilot builds o Pilot specific training codeo Pilot specific training code o Fly dayo Fly dayo Pilot snivelsPilot snivels o Rangeso Ranges o No. aircraft of each typeo No. aircraft of each type

Obeys the law

SNAP Agents: Trade-off Exploration,SNAP Agents: Trade-off Exploration, Win-Win Scheduling Solutions Win-Win Scheduling Solutions

Flow managerFlow manager PilotsPilots AircraftAircraft MissionsMissions RangesRanges PMCFPMCF SimulatorsSimulators Sim. MonitorsSim. Monitors ODOODO OrdnanceOrdnance AcademicsAcademics

Yearly Training PlanYearly Training PlanTEEPTEEP

Flight Hour ProgramFlight Hour Program

Monthly Training PlanMonthly Training Plan

Weekly Training PlanWeekly Training Plan

Daily Flight ScheduleDaily Flight Schedule

Active Flight ScheduleActive Flight Schedule

Daily ScheduleDaily Schedule

Produces schedules

Weekly Sched.Weekly Sched.

Monthly Sched.Monthly Sched.

ElectronicElectronicFeed toFeed to

MaintenanceMaintenance

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Architectural Challenges• Scaling

– Three Guarantees1. No Single Cognitive Architecture will handle all problems

Heterogeneous collection of Cognitive Architectures2. Not all activity will occur within Cognitive Architecture

Cognitive Architecture must interface with COTS & GOTS systems3. Your Cognitive Arch. will be a component in some bigger system

Cognitive Architecture must operate in an imposed architecture• Architectural Extension

– Identify points of variabity– Provide enumerated choices across that variability and procedural alternatives

• Heterogeneous Composition– API for embedding one architecture as a component within another– Sharing/adding Knowledge across architectures

or across heterogeneous components• Handling uncertainty• Security