Probabilistic Reasoning in Multiagent Systems
Probabilistic Reasoning in Multiagent Systems
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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.
- Author is pre-eminent authority on the subject, and initiated the research on the framework presented in this book
- Comprehensive book that addresses subject of probabilistic inference by multiple agents using graphical knowledge representations
- Multi-agent systems will be important in the future due to the cost of reduction of computers and networking
Reviews & endorsements
Review of the hardback: '… this is a valuable and welcome comprehensive guide to the state-of-the-art in applying belief networks.' Kybernetes
Review of the hardback: '… the well-balanced treatment of multiagent systems will make the book useful to both theoretical computer scientists and the more applied artificial intelligence community. Moreover, the interdisciplinary nature of the subject makes it relevant not only to computer scientists but also to people from operations research and microeconomics (social choice and game theory in particular). The book easily deserves to be on the shelf of any modern theoretical computer scientist.' SIGACT News
Product details
January 2005Adobe eBook Reader
9780511030147
0 pages
0kg
This ISBN is for an eBook version which is distributed on our behalf by a third party.
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.
