A REASONING MODEL BASED ON AN EXTENDED DEMPSTER-SHAFER THEORY

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Abstract

The Dempster-Shafer (D-S) theory of evidence suggests a coherent approach to aggregate evidence bearing on groups of mutually exclusive hypotheses; however, the uncertain relationships between evidence and hypotheses are difficult to represent in applications of the theory. In this paper, we extend the multivalued mapping in the D-S theory to a probabilistic one that uses conditional probabilities to express the uncertain associations. In addition, Dempster's rule is used to combine belief update rather than absolute belief to obtain results consistent with Bayes' theorem. The combined belief intervals form probability bounds under two conditional independence assumptions. Our model can be applied to expert systems that contain sets of mutually exclusive and hierarchies.

Original languageEnglish (US)
Pages125-131
Number of pages7
StatePublished - 1986
Event5th National Conference on Artificial Intelligence, AAAI 1986 - Philadelphia, United States
Duration: Aug 11 1986Aug 15 1986

Conference

Conference5th National Conference on Artificial Intelligence, AAAI 1986
Country/TerritoryUnited States
CityPhiladelphia
Period8/11/868/15/86

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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