Probabilistic reasoning in multiagent systems

A Graphical Models Approach

Yang Xiang

294 pages, parution le 25/10/2002

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Résumé

This book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning.
Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artificial 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 paradigm 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 from a decade's research.
The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

Contents

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.

L'auteur - Yang Xiang

Associate professor of Computing and information science at the university of Guelph

Caractéristiques techniques

	PAPIER
Éditeur(s)	Cambridge University Press
Auteur(s)	Yang Xiang
Parution	25/10/2002
Nb. de pages	294
Format	18 x 25
Couverture	Relié
Poids	760g
Intérieur	Noir et Blanc
EAN13	9780521813082
ISBN13	978-0-521-81308-2