Buch, Englisch, 306 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 535 g
A Graphical Models Approach
Buch, Englisch, 306 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 535 g
ISBN: 978-0-521-15390-4
Verlag: Cambridge University Press
• 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
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.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Operations Research Graphentheorie
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
Weitere Infos & Material
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.
