A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Publisher:   Morgan and Claypool Publishers
Number Of Pages:   84
Publication Date:   2007-08-31
Sales Rank:  
ISBN / ASIN:   1598295268
EAN:   9781598295269
Binding:   Paperback
Manufacturer:   Morgan and Claypool Publishers
Studio:   Morgan and Claypool Publishers
ABSTRACT

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner.
The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of single-agent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding .eld of multiagent reinforcement learning.

The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture. Nikos Vlassis is Assistant Professor at the Department of Production Engineering and Management at the Technical University of Crete, Greece. His email is vlassis@dpem.tuc.gr

KEYWORDS

Multiagent Systems, Distributed Artificial Intelligence, Game Theory, Decision Making under Uncertainty, Coordination, Knowledge and Information, Mechanism Design, Reinforcement Learning.

CONTENTS

Preface

1. Introduction
1.1 Multiagent Systems and Distributed AI
1.2 Characteristics of Multiagent Systems
1.2.1 Agent Design
1.2.2 Environment
1.2.3 Perception
1.2.4 Control
1.2.5 Knowledge
1.2.6 Communication
1.3 Applications
1.4 Challenging Issues
1.5 Notes and Further Reading

2. Rational Agents
2.1 What is an Agent?
2.2 Agents as Rational Decision Makers
2.3 Observable Worlds and the Markov Property
2.3.1 Observability
2.3.2 The Markov Property
2.4 Stochastic Transitions and Utilities
2.4.1 From Goals to Utilities
2.4.2 Decision Making in a Stochastic World
2.4.3 Example: A Toy World
2.5 Notes and Further Reading

3. StrategicGames
3.1 Game Theory
3.2 Strategic Games
3.3 Iterated Elimination of Dominated Actions
3.4 Nash Equilibrium
3.5 Notes and Further Reading

4. Coordination
4.1 Coordination Games
4.2 Social Conventions
4.3 Roles
4.4 Coordination Graphs
4.4.1 Coordination by Variable Elimination
4.4.2 Coordination by Message Passing
4.5 Notes and Further Reading

5. Partial Observability
5.1 Thinking Interactively
5.2 Information and Knowledge
5.3 Common Knowledge
5.4 Partial Observability and Actions
5.4.1 States and Observations
5.4.2 Observation Model
5.4.3 Actions and Policies
5.4.4 Payoffs
5.5 Notes and Further Reading

6. Mechanism Design
6.1 Self-Interested Agents
6.2 The Mechanism Design Problem
6.2.1 Example: An Auction
6.3 The Revelation Principle
6.3.1 Example: Second-price Sealed-bid (Vickrey) Auction
6.4 The Vickrey–Clarke–Groves Mechanism
6.4.1 Example: Shortest Path
6.5 Notes and Further Reading

7. Learning
7.1 Reinforcement Learning
7.2 Markov Decision Processes
7.2.1 Value Iteration
7.2.2 Q-learning
7.3 Markov Games
7.3.1 Independent Learning
7.3.2 Coupled Learning
7.3.3 Sparse Cooperative Q-learning
7.4 The roblem of Exploration
7.5 Notes and Further Reading

Bibliography
Author Biography
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