Title:
Multiagent systems : a modern approach to distributed artificial intelligence
Publication Information:
Cambridge, Mass. : The MIT Press, 1999
ISBN:
9780262232036
Added Author:
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000004209346 | QA76.76.I58 M84 1999 | Open Access Book | Book | Searching... |
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Summary
Summary
This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence that is suitable as a textbook.
Author Notes
Gerhard Weiss is a Research Scientist in the Computer Science Department at the Technical University of Munich.
Table of Contents
Contributing Authors |
Preface |
Prologue |
Part I Basic Themes |
1 Intelligent Agents |
1.1 Introduction |
1.2 What Are Agents? |
1.2.1 Examples of Agents |
1.2.2 Intelligent Agents |
1.2.3 Agents and Objects |
1.2.4 Agents and Expert Systems |
1.3 Abstract Architectures for Intelligent Agents |
1.3.1 Purely Reactive Agents |
1.3.2 Perception |
1.3.3 Agents with State |
1.4 Concrete Architectures for Intelligent Agents |
1.4.1 Logic-Based Architectures |
1.4.2 Reactive Architectures |
1.4.3 Belief-Desire-Intention Architectures |
1.4.4 Layered Architectures |
1.5 Agent Programming Languages |
1.5.1 Agent-Oriented Programming |
1.5.2 Concurrent METATEM |
1.6 Conclusions |
1.7 Exercises |
1.8 References |
2 Multiagent Systems and Societies of Agents |
2.1 Introduction |
2.1.1 Motivations |
2.1.2 Characteristics of Multiagent Environments |
2.2 Agent Communications |
2.2.1 Coordination |
2.2.2 Dimensions of Meaning |
2.2.3 Message Types |
2.2.4 Communication Levels |
2.2.5 Speech Acts |
2.2.6 Knowledge Query and Manipulation Language (KQML) |
2.2.7 Knowledge Interchange Format (KIF) |
2.2.8 Ontologies |
2.2.9 Other Communication Protocols |
2.3 Agent Interaction Protocols |
2.3.1 Coordination Protocols |
2.3.2 Cooperation Protocols |
2.3.3 Contract Net |
2.3.4 Blackboard Systems |
2.3.5 Negotiation |
2.3.6 Multiagent Belief Maintenance |
2.3.7 Market Mechanisms |
2.4 Societies of Agents |
2.5 Conclusions |
2.6 Exercises |
2.7 References |
3 Distributed Problem Solving and Planning |
3.1 Introduction |
3.2 Example Problems |
3.3 Task Sharing |
3.3.1 Task Sharing in the Toll Problem |
3.3.2 Task Sharing in Heterogeneous Systems |
3.3.3 Task Sharing for DSNE |
3.3.4 Task Sharing for Interdependent Tasks |
3.4 Result Sharing |
3.4.1 Functionally Accurate Cooperation |
3.4.2 Shared Repositories and Negotiated Search |
3.4.3 Distributed Constrained Heuristic Search |
3.4.4 Organizational Structuring |
3.4.5 Communication Strategies |
3.4.6 Task Structures |
3.5 Distributed Planning |
3.5.1 Centralized Planning for Distributed Plans |
3.5.2 Distributed Planning for Centralized Plans |
3.5.3 Distributed Planning for Distributed Plans |
3.6 Distributed Plan Representations |
3.7 Distributed Planning and Execution |
3.7.1 Post-Planning Coordination |
3.7.2 Pre-Planning Coordination |
3.7.3 Interleaved Planning, Coordination, and Execution |
3.7.4 Runtime Plan Coordination Without Communication |
3.8 Conclusions |
3.9 Exercises |
3.10 References |
4 Search Algorithms for Agents |
4.1 Introduction |
4.2 Constraint Satisfaction |
4.2.1 Definition of a Constraint Satisfaction Problem |
4.2.2 Filtering Algorithm |
4.2.3 Hyper-Resolution-Based Consistency Algorithm |
4.2.4 Asynchronous Backtracking |
4.2.5 Asynchronous Weak-Commitment Search |
4.3 Path-Finding Problem |
4.3.1 Definition of a Path-Finding Problem |
4.3.2 Asynchronous Dynamic Programming |
4.3.3 Learning Real-Time A* |
4.3.4 Real-Time A* |
4.3.5 Moving Target Search |
4.3.6 Real-Time Bidirectional Search |
4.3.7 Real-Time Multiagent Search |
4.4 Two-Player Games |
4.4.1 Formalization of Two-Player Games |
4.4.2 Minimax Procedure |
4.4.3 Alpha-Beta Pruning |
4.5 Conclusions |
4.6 Exercises |
4.7 References |
5 Distributed Rational Decision Making |
5.1 Introduction |
5.2 Evaluation Criteria |
5.2.1 Social Welfare |
5.2.2 Pareto Efficiency |
5.2.3 Individual Rationality |
5.2.4 Stability |
5.2.5 Computational Efficiency |
5.2.6 Distribution and Communication Efficiency |
5.3 Voting |
5.3.1 Truthful Voters |
5.3.2 Strategic (Insincere) Voters |
5.4 Auctions |
5.4.1 Auction Settings |
5.4.2 Auction Protocols |
5.4.3 Efficiency of the Resulting Allocation |
5.4.4 Revenue Equivalence and Non-Equivalence |
5.4.5 Bidder Collusion |
5.4.6 Lying Auctioneer |
5.4.7 Bidders Lying in Non-Private-Value Auctions |
5.4.8 Undesirable Private Information Revelation |
5.4.9 Roles of Computation in Auctions |
5.5 Bargaining |
5.5.1 Axiomatic Bargaining Theory |
5.5.2 Strategic Bargaining Theory |
5.5.3 Computation in Bargaining |
5.6 General Equilibrium Market Mechanisms |
5.6.1 Properties of General Equilibrium |
5.6.2 Distributed Search for a General Equilibrium |
5.6.3 Speculative Strategies in Equilibrium Markets |
5.7.1 Task Allocation Negotiation |
5.7.2 Contingency Contracts and Leveled Commitment Contracts |
5.8 Coalition Formation |
5.8.1 Coalition Formation Activity 1: Coalition Structure Generation |
5.8.2 Coalition Formation Activity 2: Optimization within a Coalition |
5.8.3 Coalition Formation Activity 3: Payoff Division |
5.9 Conclusions |
5.10 Exercises |
5.11 References |
6 Learning in Multiagent Systems |
6.1 Introduction |
6.2 A General Characterization |
6.2.1 Principal Categories |
6.2.2 Differencing Features |
6.2.3 The Credit-Assignment Problem |
6.3 Learning and Activity Coordination |
6.3.1 Reinforcement Learning |
6.3.2 Isolated, Concurrent Reinforcement Learners |
6.3.3 Interactive Reinforcement Learning of Coordination |
6.4 Learning about and from Other Agents |
6.4.1 Learning Organizational Roles |
6.4.2 Learning in Market Environments |
6.4.3 Learning to Exploit an Opponent |
6.5 Learning and Communication |
6.5.1 Reducing Communication by Learning |
6.5.2 Improving Learning by Communication |
6.6 Conclusions |
6.7 Exercises |
6.8 References |
7 Computational Organization Theory |
7.1 Introduction |
7.1.1 What Is an Organization? |
7.1.2 What Is Computational Organization Theory? |
7.1.3 Why Take a Computational Approach? |
7.2 Organizational Concepts Useful in Modeling Organizations |
7.2.1 Agent and Agency |
7.2.2 Organizational Design |
7.2.3 Task |
7.2.4 Technology |
7.3 Dynamics |
7.4 Methodological Issues |
7.4.1 Virtual Experiments and Data Collection |
7.4.2 Validation and Verification |
7.4.3 Computational Frameworks |
7.5 Conclusions |
7.6 Exercises |
7.7 References |
8 Formal Methods in DAI: Logic-Based Representation and Reasoning |
8.1 Introduction |
8.2 Logical Background |
8.2.1 Basic Concepts |
8.2.2 Propositional and Predicate Logic |
8.2.3 Modal Logic |
8.2.4 Deontic Logic |
8.2.5 Dynamic Logic |
8.2.6 Temporal Logic |
8.3 Cognitive Primitives |
8.3.1 Knowledge and Beliefs |
8.3.2 Desires and Goals |
8.3.3 Intentions |
8.3.4 Commitments |
8.3.5 Know-How |
8.3.6 Sentential and Hybrid Approaches |
8.3.7 Reasoning with Cognitive Concepts |
8.4 BDI Implementations |
8.4.1 Abstract Architecture |
8.4.2 Practical System |
8.5 Coordination |
8.5.1 Architecture |
8.5.2 Specification Language |
8.5.3 Common Coordination Relationships |
8.6 Communications |
8.6.1 Semantics |
8.6.2 Ontologies |
8.7 Social Primitives |
8.7.1 Teams and Organizational Structure |
8.7.2 Mutual Beliefs and Joint Intentions |
8.7.3 Social Commitments |
8.7.4 Group Know-How and Intentions |
8.8 Tools and Systems |
8.8.1 Direct Implementations |
8.8.2 Partial Implementations |
8.8.3 Traditional Approaches |
8.9 Conclusions |
8.10 Exercises |
References |
9 Industrial and Practical Applications of DAI |
9.1 Introduction |
9.2 Why Use DAI in Industry? |
9.3 Overview of the Industrial Life-Cycle |
9.4 Where in the Life Cycle Are Agents Used? |
9.4.1 Questions that Matter |
9.4.2 Agents in Product Design |
9.4.3 Agents in Planning and Scheduling |
9.4.4 Agents in Real-Time Control |
9.5 How Does Industry Constrain the Life Cycle of an Agent-Based System? |
9.5.1 Requirements, Positioning, and Specification |
9.5.2 Design: the Conceptual Context |
9.5.3 Design: the Process |
9.5.4 System Implementation |
9.5.5 System Operation |
9.6 Development Tools |
9.7 Conclusions |
9.8 Exercises |
9.9 References |
Part II Related Themes |
10 Groupware and Computer Supported Cooperative Work |
10.1 Introduction |
10.1.1 Well-Known Groupware Examples |
10.2 Basic Definitions |
10.2.1 Groupware |
10.2.2 Computer Supported Cooperative Work (CSCW) |
10.3 Aspects of Groupware |
10.3.1 Keepers |
10.3.2 Coordinators |
10.3.3 Communicators |
10.3.4 Team-Agents |
10.3.5 Agent Models |
10.3.6 An Example of Aspect Analysis of a Groupware |
10.4 Multi-Aspect Groupware |
10.4.1 Chautauqua -- A Multi-Aspect System |
10.5 Social and Group Issues in Designing Groupware Systems |
10.6 Supporting Technologies and Theories |
10.6.1 Keepers |
10.6.2 Coordinators |
10.6.3 Communicators |
10.6.4 Team-Agents |
10.7 Other Taxonomies of Groupware |
10.7.1 Space/Time Matrix |
10.7.2 Application Area |
10.8 Groupware and Internet |
10.8.1 Internet as Infrastructure |
10.8.2 Internet as Presumed Software |
10.9 Conclusions |
10.9.1 Incorporating Communicators into Keepers |
10.9.2 Incorporating Keepers and Communicators into Coordinators |
10.9.3 Future Research on Agents |
10.9.4 Future Research on Keepers |
10.10 Exercises |
10.11 References |
11 Distributed Models for Decision Support |
11.1 Introduction |
11.2 Decision Support Systems |
11.2.1 The Decision Support Problem |
11.2.2 Knowledge-Based Decision Support |
11.2.3 Distributed Decision Support Models |
11.3 An Agent Architecture for Distributed DSSs |
11.3.1 Information Model |
11.3.2 Knowledge Model |
11.3.3 Control Model |
11.4 Application Case Studies |
11.4.1 Environmental Emergency Management |
11.4.2 Energy Management |
11.4.3 Road Traffic Management |
11.5 Conclusions |
11.6 Exercises |
11.7 References |
12 Concurrent Programming for DAI |
12.1 Introduction |
12.2 Defining Multiagent Systems |
12.3 Actors |
12.3.1 Semantics of Actors |
12.3.2 Equivalence of Actor Systems |
12.3.3 Actors and Concurrent Programming |
12.4 Representing Agents as Actors |
12.4.1 Mobility of Actors |
12.4.2 Resource Model |
12.5 Agent Ensembles |
12.5.1 Customizing Execution Contexts |
12.5.2 Interaction Protocols |
12.5.3 Coordination |
12.5.4 Naming and Groups |
12.6 Related Work |
12.7 Conclusions |
12.8 Exercises |
12.9 References |
13 Distributed Control Algorithms for AI |
13.1 Introduction |
13.1.1 Model of Computation |
13.1.2 Complexity Measures |
13.1.3 Examples of Distributed Architectures in AI |
13.2 Graph Exploration |
13.2.1 Depth-First Search |
13.2.2 Pseudo-Fast Exploration: the Echo Algorithm |
13.2.3 Searching for Connectivity Certificates |
13.3 Termination Detection |
13.3.1 Problem Definition |
13.3.2 Tracing Algorithms |
13.3.3 Probe Algorithms |
13.4 Distributed Arc Consistency and CSP |
13.4.1 Constraint Satisfaction and Arc Consistency |
13.4.2 The AC4 Algorithm |
13.4.3 The Distributed AC4 Algorithm |
13.4.4 Termination Detection |
l3.4.5 Partitioning for Multiprocessor Computers |
13.4.6 Distributed Constraint Satisfaction Algorithm |
13.5 Distributed Graph Processing |
13.5.1 The Problem: Loop Cutset |
13.5.2 Distributed Execution of the Algorithm |
13.5.3 Complexity and Conclusions |
13.6 Conclusions |
13.7 Exercises |
13.8 References |
Glossary |
Subject Index |