Synthesis Lectures on Artificial Intelligence and Machine Learning: A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence (PDF)
(Sprache: Englisch)
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...
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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.
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.
Autoren-Porträt von Nikos Vlassis
Department of Production Engineering and Management, Technical University of Crete
Bibliographische Angaben
- Autor: Nikos Vlassis
- 2007, 71 Seiten, Englisch
- Verlag: Morgan & Claypool Publishers
- ISBN-10: 1598295276
- ISBN-13: 9781598295276
- Erscheinungsdatum: 01.06.2007
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Größe: 0.75 MB
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Englisch
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