Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach
Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a decade's research.
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Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach
Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a decade's research.
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Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

by Yang Xiang
Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

by Yang Xiang

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$148.00 
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Overview

Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a decade's research.

Product Details

ISBN-13: 9780521813082
Publisher: Cambridge University Press
Publication date: 08/26/2002
Pages: 308
Product dimensions: 7.01(w) x 10.00(h) x 0.75(d)

Table of Contents

Preface; 1. Introduction; 2. Bayesian networks; 3. Belief updating and cluster graphs; 4. Junction tree representation; 5. Belief updating with junction trees; 6. Multiply sectioned Bayesian networks; 7. Linked junction forests; 8. Distributed multi-agent inference; 9. Model construction and verification; 10. Looking into the future; Bibliography; Index.
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