Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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Elsevier Science, Sep 1, 1988 - Computers - 584 pages
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Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.

The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.


Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

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Contents

UNCERTAINTY IN Al SYSTEMS AN OVERVIEW
1
MERITS DEFICIENCIES AND REMEDIES
4
13 INTENSIONAL SYSTEMS AND NETWORK REPRESENTATIONS
12
Copyright

69 other sections not shown

Other editions - View all

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
Limited preview - 1988
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
Limited preview - 2014
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
Snippet view - 1988

Common terms and phrases

activated analysis answer applied assess assigned associated assume assumptions axioms Bayesian network belief birds calculate called causal causes Chapter combination common complete computed conclusion conditional conditional probabilities connected consequences consider consistent constraints construct contains corresponding decision default defined dependencies determine direct distribution equal estimate evidence example explanation expressed extension fact Figure function given graph independent individual inference influence instantiated interpretation knowledge likelihood logic Markov means measures messages method natural node normally observed obtained ordering parameters parents Pearl position possible probabilistic probability problem propagation properties proposition query reasoning relation relationships remain represent representation requires result rule semantics sentences separation sources specified stands statements structure Theorem theory tree TRUE uncertainty updating variables weight yields

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Bibliographic information

QR code for Probabilistic Reasoning in Intelligent Systems
TitleProbabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
AuthorJudea Pearl
Editionillustrated, reprint, revised
PublisherElsevier Science, 1988
ISBN1558604790, 9781558604797
Length584 pages
Subjects ›  › 

Computers / Artificial Intelligence / General
  
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