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 Collaboration in Multi-Agent Systems

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) 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

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 unconstrained Type 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 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 systems 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 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.

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 Pollacks mental state of plans Defines the concept of a shared plan Describes the entire web of a teams 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 Model

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

Proposed Framework Components The Conversational Model Contents A Query Interaction Protocol A Set of Collaborative Conversational Patterns A Collaborative Conversational Policy

Proposed Framework Components The Conversational Model Conversational Patterns

Proposed Framework Components The Conversational Model Conversational 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

Proposed Framework Integration into MAS Architectures Agent X Internal Representation A Agent X Internal Representation B Agent X Internal Representation B Facilitator for Internal Representation A Facilitator for Internal Representation B All Agents use Framework Implementation E2 Agent X Internal Representation A Framework Implementation E1 Agent Y Internal Representation B Framework Implementation E1 Agent Z Internal Representation B Framework Implementation E2 Facilitator for Implementation E1 to E2 Facilitator for Implementation E2 to E1 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

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 Implementation Components Proposed Ontology – Object Model

Proposed Framework Implementation Components Proposed Ontology - Frames

Proposed Framework Implementation Components Modal and Temporal Operators

Proposed Framework Implementation Components BRL Language Grammar

Proposed Framework Implementation Components Speech Acts and The Inquire Mechanism InitiatorParticipant AParticipant B A. inquire B.1 inform B.2 inform

Proposed Framework Implementation Components A Structured Message format based on XML

Proposed Framework Implementation The Components Message 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-Trade Mapping the Purchase Process to CPS Model Phases

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-Trade Trade Team Goal Hierarchy perform payment G1.1 receive payment G1.2 receive merchandise G1.4 settle buyer part of transaction G1.6 settle merchant part of transaction G1.5 deliver merchandise G1.3 goal dependency perform trade Goal G1

Proposed Case Study Teamwork in e-Trade A 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-Trade A Teamwork Knowledge-Level Model Agent Actions Attributes Actions Identification Agent Identification Parent Action Actions Type Actions Dependency

Proposed Case Study Teamwork in e-Trade A 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-Trade A Teamwork Knowledge-Level Model Agent Intention Attributes Intention Identification Agent Identification Intention Type Intention Preconditions Intention Post ConditionsA

Proposed Case Study Teamwork in e-Trade A 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

Questions

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