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A Framework for Agent Collaboration in Multi-Agent Systems

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A Framework for Agent Collaboration in Multi-Agent Systems

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  1. A Framework for Agent Collaboration in Multi-Agent SystemsSubmitted by:Mohamed Gamaleldin AtwanySupervised 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 ScienceJuly 2002

  2. A Framework for Agent Collaboration in Multi-Agent Systems Mohamed.Atwany@acm.org http://www.geocities.com/matwany/macf.ppt

  3. The Agenda • Introduction to Agents and Multi-Agent Systems • Multi-Agent Collaboration • Proposed Multi-Agent Collaboration Framework • Proposed Framework Implementation • The Case Study: e-Trade Agent Team • Summary and Conclusion

  4. 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 Introduction to Agents and Multi-Agent SystemsDefining Agents An agent is a virtual or physical computational entity that

  5. Introduction to Agents and Multi-Agent SystemsTypes of Agents • Cognitive Agents • Intentional (Rational) agents Have explicit goals motivating their actions • Module-based agents Reflexive 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

  6. Introduction to Agents and Multi-Agent SystemsDefining 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

  7. 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 SystemsOO Paradigm vs. Agent Paradigm

  8. Introduction to Agents and Multi-Agent SystemsDefining Multi - Agent Systems A 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

  9. Introduction to Agents and Multi-Agent SystemsKey Issues in Multi - Agent Systems • Communication • Interaction • Coordination interactions • Cooperation interactions • Negotiation interactions • Organization interactions

  10. Introduction to Agents and Multi-Agent SystemsKey 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)

  11. Introduction to Agents and Multi-Agent SystemsKey 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

  12. Introduction to Agents and Multi-Agent SystemsKey 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

  13. Introduction to Agents and Multi-Agent SystemsKey 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 either • Adding a new agent could result in an increase in performance levels of the group • Agent actions serve to avoid or to solve potential or actual conflicts.

  14. Introduction to Agents and Multi-Agent SystemsKey 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.

  15. Introduction to Agents and Multi-Agent SystemsKey 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

  16. Introduction to Agents and Multi-Agent SystemsKey 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

  17. Introduction to Agents and Multi-Agent SystemsApplications of Multi-Agent Systems • Problem Solving • Multi-Agent Simulation • The Construction of synthetic worlds • Collective robotics • Kinetic program design

  18. Introduction to Agents and Multi-Agent SystemsCollaboration 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.

  19. Introduction to Agents and Multi-Agent SystemsCollaboration 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

  20. Introduction to Agents and Multi-Agent SystemsCollaboration 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

  21. Introduction to Agents and Multi-Agent SystemsCollaboration 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

  22. Proposed FrameworkProposed 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

  23. Proposed FrameworkThe 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

  24. Proposed FrameworkOveral Object Model

  25. Proposed FrameworkComponents • 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

  26. _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ intend action drop intention to action Execution mutual belief that the goal is End State achieved, unachievable, or irrelevant drop intention jointly intend to main joint main action action Post-Planning joint drop joint commitment to commitment to joint commitment to subaction main action main action individual commitment to subaction Planning no mutual team belief that formation team T conditions team cannot achieve P can are matched achieve P Pre-Planning State State intiator agent recognizes problem Proposed FrameworkComponentsThe State Model

  27. Proposed FrameworkComponentsThe Conversational Model Contents • A Query Interaction Protocol • A Set of Collaborative Conversational Patterns • A Collaborative Conversational Policy

  28. Proposed FrameworkComponentsThe Conversational ModelConversational Patterns

  29. Proposed FrameworkComponentsThe Conversational ModelConversational Policy The 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

  30. Agent X Internal Representation A Framework Implementation E1 Agent Y Internal Representation B Framework Implementation E1 Agent Z Internal Representation B Framework Implementation E2 Agent X Internal Representation A Agent X Internal Representation B Agent X Internal Representation B All Agents use Framework Implementation E2 Facilitator for Internal Representation A Facilitator for Internal Representation B Facilitator for Implementation E1 to E2 Facilitator for Implementation E2 to E1 Proposed FrameworkIntegration into MAS Architectures Scenario #1: All agents use the same framework implementation Facilitators 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

  31. Proposed Framework ImplementationThe 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

  32. Proposed Framework ImplementationComponentsProposed Ontology – Object Model

  33. Proposed Framework ImplementationComponentsProposed Ontology - Frames

  34. Proposed Framework ImplementationComponentsModal and Temporal Operators

  35. Proposed Framework ImplementationComponentsBRL Language Grammar

  36. Initiator Participant A Participant B A. inquire B.1 inform B.2 inform Proposed Framework ImplementationComponentsSpeech Acts and The Inquire Mechanism

  37. Proposed Framework ImplementationComponentsA Structured Message format based on XML

  38. Proposed Framework ImplementationThe ComponentsMessage encoding/decoding facility object model

  39. Proposed Case StudyTeamwork 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

  40. Proposed Case StudyTeamwork in e-TradeMapping the Purchase Process to CPS Model Phases

  41. Proposed Case StudyTeamwork 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

  42. perform trade Goal G1 goal dependency perform payment G1.1 settle buyer part of transaction G1.6 receive payment G1.2 deliver merchandise G1.3 settle merchant part of transaction G1.5 receive merchandise G1.4 Proposed Case StudyTeamwork in e-TradeTrade Team Goal Hierarchy

  43. Proposed Case StudyTeamwork in e-TradeA Teamwork Knowledge-Level Model Agent Goal Attributes • Goal Identification • Agent Identification • Goal Type • Goal Addition Trigger • Goal Drop Trigger

  44. Proposed Case StudyTeamwork in e-TradeA Teamwork Knowledge-Level Model Agent Actions Attributes • Actions Identification • Agent Identification • Parent Action • Actions Type • Actions Dependency

  45. Proposed Case StudyTeamwork in e-TradeA Teamwork Knowledge-Level Model Agent Commitment Attributes • Commitment Identification • Agent Identification • Commitment Type • Commitment Addition Trigger • Commitment Drop Trigger

  46. Proposed Case StudyTeamwork in e-TradeA Teamwork Knowledge-Level Model Agent Intention Attributes • Intention Identification • Agent Identification • Intention Type • Intention Preconditions • Intention Post ConditionsA

  47. Proposed Case StudyTeamwork in e-TradeA Teamwork Knowledge-Level Model Agent Belief Attributes • Belief Identification • Agent Identification • Belief Addition Trigger • Belief Drop Trigger

  48. Develop prototype Develop Input Scenarios Verify prototype Develop MAS Verify MAS Proposed Case StudyTeamwork in e-Trade • An Approach for the Specification and Development of MAS Collaborative Behavior

  49. Proposed Case StudyTeamwork 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

  50. Proposed Case StudyTeamwork 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

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