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Author: Brian Falkenhainer Publisher: ISBN: Category : Artificial intelligence Languages : en Pages : 30
Book Description
This paper addresses the problem of probabilistic reasoning as it applies to Truth Maintenance Systems. A Belief Maintenance System has been constructed which manages a current set of probabilistic beliefs in much the same way that a TMS manages a set of true/false beliefs. Such a system may be thought of as a generalization of a Truth Maintenance System. It enables one to reason using normal two or three-valued logic or using probabilistic values to represent partial belief. The design of the Belief Maintenance System is described and some problems are discussed which require further research. Finally, some examples are presented, which show the utility of such a system. Keywords: Problem solving, Artificial intelligence.
Author: Brian Falkenhainer Publisher: ISBN: Category : Artificial intelligence Languages : en Pages : 30
Book Description
This paper addresses the problem of probabilistic reasoning as it applies to Truth Maintenance Systems. A Belief Maintenance System has been constructed which manages a current set of probabilistic beliefs in much the same way that a TMS manages a set of true/false beliefs. Such a system may be thought of as a generalization of a Truth Maintenance System. It enables one to reason using normal two or three-valued logic or using probabilistic values to represent partial belief. The design of the Belief Maintenance System is described and some problems are discussed which require further research. Finally, some examples are presented, which show the utility of such a system. Keywords: Problem solving, Artificial intelligence.
Author: L.N. Kanal Publisher: Elsevier ISBN: 1483296539 Category : Computers Languages : en Pages : 474
Book Description
This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.
Author: P.-Z. Wang Publisher: World Scientific ISBN: 9789810236632 Category : Mathematics Languages : en Pages : 408
Book Description
The ?Fuzzy Explosion? emanating from Japan has compelled more people than ever to ponder the meaning and potential of fuzzy engineering. Scientists all over are now beginning to harness the power of fuzzy recognition and decision-making ? reminescent of the way the human mind works ? in computer applications.In this book a blue-ribbon list of contributors discusses the latest developments in topics such as possibility logic programming, truth-valued flow inference, fuzzy neural-logic networks and default knowledge representation. This volume is the first in a series aiming to document advances in fuzzy set theory and its applications.
Author: Judea Pearl Publisher: Elsevier ISBN: 0080514898 Category : Computers Languages : en Pages : 573
Book Description
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.
Author: Michael Clarke Publisher: Springer Science & Business Media ISBN: 9783540573951 Category : Computers Languages : en Pages : 408
Book Description
In recent years it has become apparent that an important part of the theory of artificial intelligence is concerned with reasoning on the basis of uncertain, incomplete, or inconsistent information. A variety of formalisms have been developed, including nonmonotonic logic, fuzzy sets, possibility theory, belief functions, and dynamic models of reasoning such as belief revision and Bayesian networks. Several European research projects have been formed in the area and the first European conference was held in 1991. This volume contains the papers accepted for presentation at ECSQARU-93, the European Conference on Symbolicand Quantitative Approaches to Reasoning and Uncertainty, held at the University of Granada, Spain, November 8-10, 1993.
Author: Anthony Hunter Publisher: Springer ISBN: 354049426X Category : Computers Languages : en Pages : 474
Book Description
An introductory review of uncertainty formalisms by the volume editors begins the volume. The first main part of the book introduces some of the general problems dealt with in research. The second part is devoted to case studies; each presentation in this category has a well-delineated application problem and an analyzed solution based on an uncertainty formalism. The final part reports on developments of uncertainty formalisms and supporting technology, such as automated reasoning systems, that are vital to making these formalisms applicable. The book ends with a useful subject index. There is considerable synergy between the papers presented. The representative collection of case studies and associated techniques make the volume a particularly coherent and valuable resource. It will be indispensable reading for researchers and professionals interested in the application of uncertainty formalisms as well as for newcomers to the topic.
Author: Brian Falkenhainer
Author: Brian Falkenhainer
Author: 
Author: 
Author: L.N. Kanal
Author: MKP
Author: P.-Z. Wang
Author: Judea Pearl
Author: Michael Clarke
Author: William R. Wilson
Author: Anthony Hunter