Advances in the Dempster-Shafer Theory of Evidence
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More About This Title Advances in the Dempster-Shafer Theory of Evidence
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Builds on classical probability theory and offers an extremely workable solution to the many problems of artificial intelligence, concentrating on the rapidly growing areas of fuzzy reasoning and neural computing. Contains a collection of previously unpublished articles by leading researchers in the field.
English
Partial table of contents:
DEMPSTER-SHAFER THEORY OF EVIDENCE: GENERAL ISSUES.
Measures of Uncertainty in the Dempster-Shafer Theory of Evidence (G. Klir).
Comparative Beliefs (S. Wong, et al.).
Calculus with Linguistic Probabilities and Beliefs (M. Lamata & S. Moral).
FUZZIFICATION OF DEMPSTER-SHAFER THEORY OF EVIDENCE.
Rough Membership Functions (Z. Pawlak & A. Skowron).
DEMPSTER-SHAFER THEORY IN DECISION MAKING AND OPTIMIZATION.
Decision Analysis Using Belief Functions (T. Strat).
Interval Probabilities Induced by Decision Problems (T. Whalen).
DEMPSTER-SHAFER THEORY FOR THE MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS.
Using Dempster-Shafer's Belief-Function Theory in Expert Systems (P. Shenoy).
Nonmonotonic Reasoning with Belief Structures (R. Yager).
Index.
DEMPSTER-SHAFER THEORY OF EVIDENCE: GENERAL ISSUES.
Measures of Uncertainty in the Dempster-Shafer Theory of Evidence (G. Klir).
Comparative Beliefs (S. Wong, et al.).
Calculus with Linguistic Probabilities and Beliefs (M. Lamata & S. Moral).
FUZZIFICATION OF DEMPSTER-SHAFER THEORY OF EVIDENCE.
Rough Membership Functions (Z. Pawlak & A. Skowron).
DEMPSTER-SHAFER THEORY IN DECISION MAKING AND OPTIMIZATION.
Decision Analysis Using Belief Functions (T. Strat).
Interval Probabilities Induced by Decision Problems (T. Whalen).
DEMPSTER-SHAFER THEORY FOR THE MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS.
Using Dempster-Shafer's Belief-Function Theory in Expert Systems (P. Shenoy).
Nonmonotonic Reasoning with Belief Structures (R. Yager).
Index.
