| 000 | 00901namuu22002658a 4500 | |
| 001 | 000045117092 | |
| 005 | 20040906163156 | |
| 008 | 011105s2002 nyua b 001 0 eng | |
| 010 | ▼a ?1052874 | |
| 020 | ▼a 0521813085 | |
| 040 | ▼a DLC ▼c DLC ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a Q337 ▼b .X53 2002 |
| 082 | 0 0 | ▼a 006.3 ▼2 21 |
| 090 | ▼a 006.3 ▼b X6p | |
| 100 | 1 | ▼a Xiang, Yang , ▼d 1954-. |
| 245 | 1 0 | ▼a Probabilistic reasoning in multi-agent systems: ▼b a graphical models approach / ▼c Yang Xiang. |
| 260 | ▼a New York : ▼b Cambridge University Press , ▼c 2002. | |
| 263 | ▼a 0207 | |
| 300 | ▼a xii, 294 p : ▼b ill ; ▼c 26 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Distributed artificial intelligence. |
| 650 | 0 | ▼a Bayesian statistical decision theory ▼x Data processing. |
| 650 | 0 | ▼a Intelligent agents (Computer software) |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.3 X6p | Accession No. 121095978 (3회 대출) | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
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.
Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments.
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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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