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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence [electronic resource] / by Nikos Vlassis.

By: Vlassis, Nikos [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Artificial Intelligence and Machine Learning: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2007Edition: 1st ed. 2007.Description: XII, 71 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031015434.Subject(s): Artificial intelligence | Machine learning | Neural networks (Computer science)  | Artificial Intelligence | Machine Learning | Mathematical Models of Cognitive Processes and Neural NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Rational Agents -- Strategic Games -- Coordination -- Partial Observability -- Mechanism Design -- Learning.
In: Springer Nature eBookSummary: 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.
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Introduction -- Rational Agents -- Strategic Games -- Coordination -- Partial Observability -- Mechanism Design -- 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.

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