Advances in the Dempster-Shafer Theory of Evidence / Edition 1

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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 Contains a collection of previously unpublished articles by leading researchers in the field.

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Editorial Reviews

Belief functions provide a bridge between fuzzy reasoning and probability. Articles are divided into four sections: general issues, fuzzification of the theory of evidence, decision making and optimization, and management of uncertainty in knowledge-based systems. Topics include measurements of uncertainty, from rough set theory to evidence theory, rough membership functions, interval probabilities, and representation of evidence by hints. Annotation c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780471552482
  • Publisher: Wiley
  • Publication date: 3/1/1994
  • Edition number: 1
  • Pages: 608
  • Product dimensions: 6.14 (w) x 9.21 (h) x 1.38 (d)

Table of Contents

Foreword 1
1 What is Dempster-Shafer's model? 5
2 Measures of uncertainty in the Dempster-Shafer theory of evidence 35
3 Representation, independence, and combination of evidence in the Dempster-Shafer theory 51
4 Focusing versus updating in belief function theory 71
5 Combination of compatible belief functions and relation of specificity 97
6 Comparative beliefs 115
7 Calculus with linguistic probabilities and beliefs 133
8 Steps toward efficient implementation of Dempster-Shafer theory 153
9 Monte-Carlo methods make Dempster-Shafer formalism feasible 175
10 From rough set theory to evidence theory 193
11 Mass distributions on L-fuzzy sets and families of frames of discernment 239
12 Rough membership functions 251
13 Decision analysis using belief functions 275
14 On decision making using belief functions 311
15 Dynamic decision making with belief functions 331
16 Interval probabilities induced by decision problems 353
17 Constraint propagation over a restricted space of configurations, and its use in optimization 375
18 Using Dempster-Shafer's belief-function theory in expert systems 395
19 Issues of knowledge representation in Dempster-Shafer's theory 415
20 Representing heuristic knowledge and propagating beliefs in the Dempster-Shafer theory of evidence 441
21 Representation of evidence by hints 473
22 Evidential reasoning with conditional events 493
23 A calculus for mass assignments in evidential reasoning 513
24 Nonmonotonic reasoning with belief structures 533
25 How far are we from the complete knowledge? Complexity of knowledge acquisition in the Dempster-Shafer approach 555
26 Mass assignments and fuzzy sets for fuzzy databases 577
Index 595
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