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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Nikos Vlassis 作者
Morgan and Claypool Publishers
譯者
2007-7-2 出版日期
84 頁數
USD 40.00 價格
Paperback
叢書系列
9781598295269 圖書編碼

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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 著者簡介

Nikos Vlassis is Assistant Professor at the Department of Production Engineering and

Management at the Technical University of Crete, Greece. His email is vlassis@dpem.tuc.gr

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 著者簡介

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence 在線電子書 圖書描述

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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