Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

ISBN-13: 9780521813082 / Angielski / Twarda / 2002 / 308 str.

Ontario) Yang Xiang (University of Guelph

Opis

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.

Recenzje

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

Spis treści

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.