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Probabilistic reasoning in multi-agent systems: a graphical models approach

Probabilistic reasoning in multi-agent systems: a graphical models approach (3회 대출)

자료유형
단행본
개인저자
Xiang, Yang , 1954-.
서명 / 저자사항
Probabilistic reasoning in multi-agent systems: a graphical models approach / Yang Xiang.
발행사항
New York :   Cambridge University Press ,   2002.  
형태사항
xii, 294 p : ill ; 26 cm.
ISBN
0521813085
서지주기
Includes bibliographical references and index.
일반주제명
Distributed artificial intelligence. Bayesian statistical decision theory -- Data processing. Intelligent agents (Computer software)
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020 ▼a 0521813085
040 ▼a DLC ▼c DLC ▼d 211009
042 ▼a pcc
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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)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.3 X6p 등록번호 121095978 (3회 대출) 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

책소개

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.


정보제공 : Aladin

목차

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.


정보제공 : Aladin

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