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Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating

  • Hexin Lv
  • Bin Zhu
  • Yongchuan Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4529)

Abstract

This paper presents a theory called conditional Dempster-Shafer theory (CDS) for uncertain knowledge updating. In this theory, a priori knowledge about the value attained by an uncertain variable is modeled by a fuzzy measure and the evidence about the underlying uncertain variable is modeled by the Dempster-Shafer belief measure. Two operations in CDS are discussed in this paper, the conditioned combination rule and conditioning rule, which deal with evidence combining and knowledge updating, respectively. We show that conditioned combination rule and conditioning rule in CDS satisfy the property of Bayesian parallel combination.

Keywords

Uncertain Variable Combination Rule Fuzzy Measure Evidence Theory Orthogonal Idempotent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Hexin Lv
    • 1
  • Bin Zhu
    • 1
  • Yongchuan Tang
    • 2
  1. 1.College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, Zhejiang Province, 310015P.R. China
  2. 2.College of Computer Science, Zhejiang University, Hangzhou, Zhejiang Province, 310027P.R. China

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