A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

✍ Scribed by Nikos Vlassis

• Publisher: Springer
• Year: 2007
• Tongue: English
• Leaves: 77
• Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
• Edition: 1
• Category: Library

✦ Synopsis

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 singleagent 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 field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.

✦ Table of Contents

Cover
Title Page
Copyright Page
Contents
Preface
Introduction
MULTIAGENT SYSTEMS AND DISTRIBUTED AI
CHARACTERISTICS OF MULTIAGENT SYSTEMS
Agent Design
Environment
Perception
Control
Knowledge
Communication
APPLICATIONS
CHALLENGING ISSUES
NOTES AND FURTHER READING
Rational Agents
WHAT IS AN AGENT?
AGENTS AS RATIONAL DECISION MAKERS
OBSERVABLE WORLDS AND THE MARKOV PROPERTY
Observability
The Markov Property
STOCHASTIC TRANSITIONS AND UTILITIES
From Goals to Utilities
Decision Making in a Stochastic World
Example: A Toy World
NOTES AND FURTHER READING
Strategic Games
GAME THEORY
STRATEGIC GAMES
ITERATED ELIMINATION OF DOMINATED ACTIONS
NASH EQUILIBRIUM
NOTES AND FURTHER READING
Coordination
COORDINATION GAMES
SOCIAL CONVENTIONS
ROLES
COORDINATION GRAPHS
Coordination by Variable Elimination
Coordination by Message Passing
NOTES AND FURTHER READING
Partial Observability
THINKING INTERACTIVELY
INFORMATION AND KNOWLEDGE
COMMON KNOWLEDGE
PARTIAL OBSERVABILITY AND ACTIONS
States and Observations
Observation Model
Actions and Policies
Payoffs
NOTES AND FURTHER READING
Mechanism Design
SELF-INTERESTED AGENTS
THE MECHANISM DESIGN PROBLEM
Example: An Auction
THE REVELATION PRINCIPLE
Example: Second-price Sealed-bid (Vickrey) Auction
THE VICKREY--CLARKE--GROVES MECHANISM
Example: Shortest Path
NOTES AND FURTHER READING
Learning
REINFORCEMENT LEARNING
MARKOV DECISION PROCESSES
Value Iteration
Q-learning
MARKOV GAMES
Independent Learning
Coupled Learning
Sparse Cooperative Q-learning
THE PROBLEM OF EXPLORATION
NOTES AND FURTHER READING
Bibliography
Author biography