Post on 27-Mar-2015
A Framework for Agent Collaboration in Multi-Agent Systems
Submitted by:
Mohamed Gamaleldin Atwany
Supervised by:
Abdel-Aziz Khamis, Phd. Magdy Aboul-Ela, Phd.Dept. of Computer and Dept. of Computer and Information Sciences, Information Systems, Cairo University Sadat Academy for
Management Sciences
A thesis submitted to the Department of Computer Science, Institute of Statistical Studies and Research, Cairo University, in partial fulfillment of
the requirements for the degree of Master in Computer Science
July 2002
A Framework for Agent A Framework for Agent Collaboration in Multi-Collaboration in Multi-
Agent SystemsAgent Systems
Mohamed.Atwany@acm.orgMohamed.Atwany@acm.org
http://www.geocities.com/matwany/http://www.geocities.com/matwany/macf.pptmacf.ppt
The Agenda
1. Introduction to Agents and Multi-Agent Systems
2. Multi-Agent Collaboration
3. Proposed Multi-Agent Collaboration Framework
4. Proposed Framework Implementation
5. The Case Study: e-Trade Agent Team
6. Summary and Conclusion
Introduction to Agents and Multi-Agent Systems
Defining Agents
have partial representation of
the environment
perceive and act upon its
environment
may be able to reproduce itself
can communicate directly with
other agents
possess skills and can offer
services
possess resources of its own
driven by a set of tendencies
autonomous behavior
Possess behavioral flexibility
and rationality
An agent is a virtual or physical computational entity that
Introduction to Agents and Multi-Agent Systems
Types of Agents Cognitive Agents
Intentional (Rational) agentsHave explicit goals motivating their actions
Module-based agentsReflexive cognitive agents
Reactive agents Drive-based agents
Directed by motivation mechanisms Agents
Respond to stimuli from the environment, behavior guided by the local state of the world in which they are immersed
Introduction to Agents and Multi-Agent Systems
Defining Intelligent Agents Able to pursue its goals and executes its actions such that it
optimizes some given performance measure Operates flexibly and rationally in a variety of environmental
circumstances, given the information they have and their perceptual and effectual capabilities
Has explicit goals motivating its action
Introduction to Agents and Multi-Agent Systems
OO Paradigm vs. Agent Paradigm Object is the basic unit Entity state definition is
unconstrainedType of messages are unconstrained
Abstraction level is lower
Agent is the basic unit Entity state defined via
Belief, commitments, goals Types of messages include
request, inform, query Abstraction level is higher
and hence, it is more suited to the development of open systems
Introduction to Agents and Multi-Agent Systems
Defining Multi - Agent SystemsA multi-agent system is a system composed of number of interacting agents
and characterized by being comprised of the following elements
An environment
A set of passive environment objects that agents can perceive, create, destroy and modify
A number of agents representing system’s active entities
A number of relations that link objects and agents to each other
A number of operations that enables agents to perceive, produce, consume, transform and
manipulate environment objects
Laws of the universe
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi - Agent Systems Communication
Interaction
Coordination interactions
Cooperation interactions
Negotiation interactions
Organization interactions
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Communication A threefold problem involving knowledge of interaction protocol,
communication language and transport protocol
Forms the basis for interaction and social organization Speech Acts Theory
views natural human language as actions (a suggestion, a commitment, or a reply) classified to types (Assertive acts, Directive acts, …etc.)
KQML (content, communication, and message layers)
Conversations Defined as a series of communications among different agents that follows a
protocol and with some purpose A layered conversational model (protocol, conversation, and policy layers)
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Interaction An interaction situation is an assembly of behaviors
resulting from the grouping of agents acting in order to
attain their objectives, paying attention to the resources
available to them and to their individual skills
Occurs between two or more agents brought into a
dynamic relationship through a set of reciprocal actions
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Coordination Refers to either
a state of an agent community where agents’ actions fit well with each other or
to the process of achieving a state of coordination within an agent community
Agents coordinate their actions for four main reasons Agents require information and results other agents’ supply Limited resources have to be shared to optimize carried actions and
try avoid possible conflicts Enables cost reduction by eliminating pointless actions and avoiding
redundant actions Agents might have separate interdependent objectives that they need
to achieve while profiting from goal interdependencies
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Cooperation Defined as coordination among non-
antagonistic agents where participants succeed or fail together
A cooperative situation is validated if eitherAdding a new agent could result in an increase in
performance levels of the groupAgent actions serve to avoid or to solve potential
or actual conflicts.
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Negotiation Defined as
Interaction between agents based on communication for the purpose of coming to an agreement, or
A process by which a joint decision is reached by two or more agents, each trying to reach an individual goal or objective, or
Coordination among competitive or simply self-interested agents or,
As a distributed communication-based search through a space of possible solutions.
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Negotiation Is much related to distributed conflict resolution and decision-
making Requires agents to use a common language Supports cooperation and coordination between agents The Process:
Agents make proposals Proposals are commented (refined, criticized, or refuted) by other
agents other agents then communicate their possibly conflicting positions, Agents then trying to move towards agreement by making
compromises or searching for alternatives
Introduction to Agents and Multi-Agent Systems
Key Issues in Multi-Agent Systems
Organization Defined as an arrangement of relationships between
components or individuals which produce a unit, or system, endowed with qualities not apprehended at the level of the components or individuals.
An organization links, in an inter-relational manner, diverse elements or events or individuals, which thenceforth become the components of a whole.
An organization ensures a relatively high degree of interdependence and reliability, thus providing the system with the possibility of lasting for a certain length of time, despite chance disruptions
Introduction to Agents and Multi-Agent Systems
Applications of Multi-Agent Systems
Problem Solving Multi-Agent Simulation The Construction of synthetic worlds Collective robotics Kinetic program design
Introduction to Agents and Multi-Agent Systems
Collaboration in Multi-Agent Systems Defined as forms of high-level cooperation that requires the
(development of) mutual understanding and a shared view of the task being solved by several interacting entities
Collaboration occur within a team of agents cooperating to achieve some collective goal.
As a team of cooperating agents, participating agents succeed or fail together.
Sharing a mental state within a team of agents enables reasoning about their beliefs, commitments, and intentions and hence, reason about the success or failure of collaboration.
Introduction to Agents and Multi-Agent Systems
Collaboration in Multi-Agent Systems
Multi-Agent Collaboration Theories The Theory of Joint Intentions
defines logic of rational action that is intended to be used as a specification of agent design
The basic argument is that a joint activity is one that is performed by individuals sharing certain specific mental properties which affect and are affected by properties of the participants
The Shared Plans Theory several deficiencies noted in Pollack’s mental state of plans Defines the concept of a shared plan Describes the entire web of a team’s intentions and beliefs when
engaged in teamwork The Theory of Cooperative Problem Solving Process
presents a model of cooperative problem solving (CPS) characterizes agents’ mental states leading them to solicit, and
take part in, cooperative action
Introduction to Agents and Multi-Agent Systems
Collaboration in Multi-Agent Systems
Multi-Agent Collaboration Frameworks GRATE
a general framework that enables the construction of multi-agent systems for the domain of industrial process control
Applications could be built very rapidly because much of the general domain behavior is already defined
STEAM enables a team of agents to act coherently in a way that overcomes
the uncertainties of complex, dynamic environments in which team members often encounter differing, incomplete and possibly inconsistent views of the world and mental state of other agents
The Issue of Interoperability The frameworks does not support interoperability
Open systems Readiness Heterogeneous agents, no pre-specified interaction protocols, no pre-
specified organization
Introduction to Agents and Multi-Agent Systems
Collaboration in Multi-Agent Systems
The Development of a Shared Mental State
The shared mental state consists of the following set of shared knowledge structures:
a dependency graph of achievement goals a dependency graph of commitments to achieve these goals a dependency graph of actions believed to achieve these
goals a dependency graph of commitments to these actions a dependency graph of intentions of actions agents are
committed to achieve a dependency graph of mutual beliefs about goal relevance
and achievement status, status of commitments, status of intentions, and status of actions
Proposed Framework
Proposed Framework for Multi-Agent Collaboration
Scope Creating, sharing, and maintaining a shared mental state
within a team of agents
Objectives Framework based on a formal model of teamwork Support different phases of cooperative problem solving Transparent to existing interaction protocols and agent
organizations Transparent to development environments Transparent to agent architectures
Proposed Framework
The Methodology
Is based on the observation that behavior can be analyzed without any knowledge of the implementation details
The proposed framework should be based on two teamwork models
The proposed framework should adopt a layered conversational model
Proposed Framework
Overal Object ModelConceptual Framework
Framework Implementation
MAS Systems
State Model
Conversational Model
Framework Implementation Derivation Mechanism
BRL
ACL
Message Encoding & Decoding Facil ity
XML Message Format
Query Interaction Protocol
Conversational Pattern
Conversational Policy
Cooperation Mechanism
Negotiation Mechanism
Team Organization
Coordination Mechanism
Communication Mechanism
Formal Teamwork Model
Ontology
Extention Mechanism
Proposed Framework
Components
Define a pattern of interaction for information exchange that agents should follow
Define unambiguous rules for reasoning about agent and team behavior
Maintain a clear separation between the generic specification defined by the framework and possible implementations of that framework
Proposed Framework
Components
The State Model
_
Planning
_
Pre-Planning
_
Post-Planning
_
Execution
_
End State
_
individual commitment to subaction
_
joint commitment to subaction
_
team cannot achieve P
_
team
_
formation
_
conditions
_
are matched
_
no mutual
_
belief that
_
team T
_
can
_
achieve P
_
joint
_
commitment to
_
main action
_
drop joint
_
commitment to
_
main action
_
intend action
_
drop intention
_
to main joint
_
action
_
drop intention to action
_
jointly intend
_
main action
_
mutual belief that the goal is
_
achieved, unachievable, or
_
irrelevant
_
State State
_
intiator agent
_
recognizes problem
Proposed Framework
Components
The Conversational Model Contents
A Query Interaction Protocol A Set of Collaborative Conversational Patterns A Collaborative Conversational Policy
Proposed Framework
ComponentsThe Conversational Model
Conversational PatternsForm Team Conversational Pattern
This conversational pattern defines the preconditions, post-conditions,
reasoning, and mental states used to form a team.
Valid Team States
Pre-Planning
Pre-Conditions
the team formation facilitator agent has a designated goal to achieve
through team action.
Step 1: Reasoning Performed by the Team Formation Facilitator Agent
a. belief that a candidate team is able to achieve designated goal.
b. attempt to solicit assistance in order to form team to achieve designated
goal.
Step 2: Messages Sent by the Team Formation Facilitator Agent
c. announce to candidate team agents its attempt to solicit assistance in
order to achieve designated goal.
Step 3: Reasoning Performed by Other Candidate Agents
d. maintain belief about team formation facilitator announced attempt.
Proposed Framework
ComponentsThe Conversational Model
Conversational PolicyThe proposed agent conversation policy consists of the
following components: Domain and problem specific rules to be defined by
the agent developer. Teamwork rules explicitly defined by the proposed
framework state model through the definition of possible team states and the rules for reasoning about team states.
Teamwork rules defined by the Cooperating Problem Solving Theory
Teamwork rules defined by the Joint Intentions Theory
Proposed Framework
Integration into MAS Architectures
Agent X
InternalRepresentation A
Agent X
InternalRepresentation B
Agent X
InternalRepresentation B
Facilitator for Internal
Representation A
Facilitator for Internal
Representation B
All Agents use Framework
Implementation E2
Agent XInternal
Representation AFramework
Implementation E1
Agent YInternal
Representation BFramework
Implementation E1
Agent ZInternal
Representation BFramework
Implementation E2
Facilitator for Implementation
E1 to E2
Facilitator for Implementation
E2 to E1
Scenario #1:All agents use the same framework implementationFacilitators translate between internal representations and the framework BRL
Some agents use framework implementation E1, while others use framework implementation E2.Facilitators translate messages between framework implementations E1 and E2
Proposed Framework Implementation
The Components A behavior representation language for modeling agent
mental behavior (BRL) Ontology Modal and temporal operators grammar The extension mechanism
An agent communication language (ACL) Speech acts mechanisms
A message interchange format XML based message format Mapping to BRL elements
A set of facilitators and components XML message encoding/decoding facility
Proposed Framework ImplementationComponents
Proposed Ontology – Object Model
Proposed Framework ImplementationComponents
Proposed Ontology - Frames
Frame Field Name Description
belief id unique identification Belief
agent id unique identification for agent or team that owns this belief
Belief Message clause id unique identification for the message clause, see next
section
goal id unique identification Goal
agent id unique identification for agent or team that owns this belief
commitment id unique identification
agent id unique identification for agent or agent group that owns this
commitment
Commitment
goal id or action
id
unique identification for goal (action) that an agent is
committed to achieve (execute)
Proposed Framework ImplementationComponents
Modal and Temporal Operators
Modal clause Description
achievable Expresses the status of achievability of a given goal
irrelevant Expresses the status of relevance of a given goal
Exist Expresses the status of existence of a designated knowledge
element.
Temporal clause Description
Added Expresses the status of addition of a new designated knowledge
element.
Dropped Expresses the status of dropping a designated belief
Attempted Expresses the past status of attempt in the past of a designated
action as a result of an agent intention
attempting Expresses the current status of attempt in execution of a
designated action as a result of an agent intention
achieved Expresses the status of achievement of a given goal
Proposed Framework ImplementationComponents
BRL Language Grammar
<language clause>
::=
<message clause> | <BDI clause>
<message clause> ::= <added clause> | <exist clause> | <dropped
clause> | <irrelevant clause> | <achievable
clause> | <achieved clause> | <unknown clause>
| <attempted clause> | <attempting clause>
<exist clause> ::= exist (belief clause id | goal clause id |
commitment clause id | intention clause id |
attempt clause id, truth value)
<dropped clause> ::= dropped (belief clause id | goal clause id |
commitment clause id | intention clause id |
attempt clause id, truth value)
Proposed Framework ImplementationComponents
Speech Acts and The Inquire Mechanism
Initiator Participant A Participant B
A. inquire
B.1 inform
B.2 inform
Proposed Framework ImplementationComponents
A Structured Message format based on XML
<team_message team_id="trade_team" message_id="attempt_to_solicit_assistance_1">
<meta_info content_type="framework-implementation-horn-clause"/>
<delivery_info sender_id="buyer">
<recipient_list>
<recipient agent_id="merchant"/>
<recipient agent_id="delivery"/>
</recipient_list>
<reply_to_list>
<recipient agent_id="merchant"/>
<recipient agent_id="delivery"/>
<recipient agent_id="buyer"/>
</reply_to_list>
</delivery_info>
<message_content>
<message_clause truth_value="true"
Proposed Framework Implementation
The ComponentsMessage encoding/decoding facility object model
Proposed Case Study
Teamwork in e-Trade
The Problem
Trade as an Organization of Trade Agents
Trade as an Interaction of Trade Agents
Trade as a Task Environment
Trade as a Cooperative System
Trade as a Coordinated System
Collaboration Within a Trade Team
Proposed Case Study
Teamwork in e-TradeMapping the Purchase Process to CPS Model Phases
Purchase Process Phase CPS Model Phase Description
Decision to buy
merchandise
Recognition The buyer recognize the
potential for performing
purchase
Pre-team Buyer negotiates with a
group of merchants to
select the best offer, and
then, construct a trade
team in order to execute
the transaction
Negotiation phase
Planning Buyer and merchant agree
on all purchase attributes
Exchange phase
Settlement phase
execution Trade team members
execute purchase
transaction steps in a
timely coordinated manner
Proposed Case Study
Teamwork in e-Trade A Knowledge-Level Model for Reasoning
about Collaboration Consisting of a set of mental elements
categorized into beliefs, goals, commitments, and intentions and their dependencies
Specifying a number of inference rules that allow reasoning about teamwork state
Enable exchange of beliefs, goals, intentions, and commitments
Proposed Case Study
Teamwork in e-TradeTrade Team Goal Hierarchy
perform paymentG1.1
receive paymentG1.2
receive merchandise
G1.4
settle buyer part of transaction
G1.6
settle merchant part of transaction
G1.5
deliver merchandise
G1.3
goal dependencyperform trade
Goal G1
Proposed Case Study
Teamwork in e-TradeA Teamwork Knowledge-Level Model
Agent Goal Attributes Goal Identification Agent Identification Goal Type Goal Addition Trigger Goal Drop Trigger
Proposed Case Study
Teamwork in e-TradeA Teamwork Knowledge-Level Model
Agent Actions Attributes Actions Identification Agent Identification Parent Action Actions Type Actions Dependency
Proposed Case Study
Teamwork in e-TradeA Teamwork Knowledge-Level Model
Agent Commitment Attributes Commitment Identification Agent Identification Commitment Type Commitment Addition Trigger Commitment Drop Trigger
Proposed Case Study
Teamwork in e-TradeA Teamwork Knowledge-Level Model
Agent Intention Attributes Intention Identification Agent Identification Intention Type Intention Preconditions Intention Post ConditionsA
Proposed Case Study
Teamwork in e-TradeA Teamwork Knowledge-Level Model
Agent Belief Attributes Belief Identification Agent Identification Belief Addition Trigger Belief Drop Trigger
Proposed Case Study
Teamwork in e-Trade An Approach for the Specification and
Development of MAS Collaborative Behavior
Develop MAS
Verify MAS
Develop Input Scenarios
Verify prototype
Develop prototype
Proposed Case Study
Teamwork in e-Trade
Collaborative Analysis Facility Based on proposed framework and proposed
framework implementation Encode agent strategy with the prototype All possible collaborative scenarios are
encoded into program input Validate generated behavior
Proposed Case Study
Teamwork in e-Trade Case Study Results
Agent interaction and communication is crucial for maintaining a shared and consistent view of the trade problem
A common view of the goals, actions, commitments, and intentions help agents reason on teamwork activities and state
The use of conversational model helped agents reason about teamwork activities and state
The implementation enabled agents to express collaborative mental behavior, using a set of agent interaction mechanisms, and transmitted using a common message format
By reviewing generated output, the MAS developer is able to verify team and individual collaborative behavior
Summary and Conclusion Multi-agent systems are complex systems consisting of a
number of agents, each of which by itself might represent an organization consisting of one or more agents
within a MAS, agents interact in order to achieve their individual and collective goals in an act defined as collaboration
The lack of support for interoperability and open systems in existing environments
Proposed framework provides transparency to current development environment, interaction protocols, and agent organizations
Summary and Conclusion Separating framework from possible implementation enables
the evolution of implementations that match environment needs
The proposed implementation provides an ACL, a BRL, a message format, and a message encoding/decoding facility
The agent paradigm suitable for the developing of open systems as found in the domain of e-Trade
The proposed framework and the proposed framework implementation enabled the development of a MAS in the domain of e-Trade
The proposed iterative approach eases the process of specification and development of MAS collaborative behavior
QuestionsQuestions
Thank YouThank You