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

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. 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