Yang Xiang

Probabilistic Reasoning in Multiagent Systems (eBook, PDF)

A Graphical Models Approach

• Format: PDF

Produktbeschreibung

This 2002 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 increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang 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. 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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 26. August 2002
• Englisch
• ISBN-13: 9780511030147
• Artikelnr.: 38188499

Inhaltsangabe

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.