Probabilistic Reasoning in Multiagent Systems
A Graphical Models Approach
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Product details:
- Publisher Cambridge University Press
- Date of Publication 26 August 2002
- ISBN 9780521813082
- Binding Hardback
- No. of pages308 pages
- Size 254x178x19 mm
- Weight 764 g
- Language English 0
Categories
Short description:
Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments.
MoreLong description:
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.
Review of the hardback: '... this is a valuable and welcome comprehensive guide to the state-of-the-art in applying belief networks.' Kybernetes
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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