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A concise introduction to multiagent systems and distributed artificial intelligence. (English) Zbl 1198.68265
Synthesis Lectures on Artificial Intelligence and Machine Learning 2. San Rafael, CA: Morgan & Claypool Publishers (ISBN 978-1-59829-526-9/pbk; 978-1-59829-527-6/ebook). xii, 71 p. (2007).
Publisher’s description: 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.

MSC:
68T42 Agent technology and artificial intelligence
68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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