A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

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 d...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Vlassis, Nikos (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2007.
Edición:1st ed. 2007.
Colección:Synthesis Lectures on Artificial Intelligence and Machine Learning,
Temas:
Artificial intelligence.
Machine learning.
Neural networks (Computer science) .
Artificial Intelligence.
Machine Learning.
Mathematical Models of Cognitive Processes and Neural Networks.
Acceso en línea:Texto Completo

MARC

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300 |a XII, 71 p.  |b online resource. 
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490 1 |a Synthesis Lectures on Artificial Intelligence and Machine Learning,  |x 1939-4616 
505 0 |a Introduction -- Rational Agents -- Strategic Games -- Coordination -- Partial Observability -- Mechanism Design -- Learning. 
520 |a 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. 
650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
650 0 |a Neural networks (Computer science) . 
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650 2 4 |a Machine Learning. 
650 2 4 |a Mathematical Models of Cognitive Processes and Neural Networks. 
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