Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating

Authors:
Hexin Lv;Bin Zhu;Yongchuan Tang
Affiliations:
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, Zhejiang Province, 310015, P.R. China;College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, Zhejiang Province, 310015, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, Zhejiang Province, 310027, P.R. China
Venue:
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Year:
2007

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Abstract

This paper presents a theory called conditional Dempster-Shafer theory (CDS) for uncertain knowledge updating. In this theory, a prioriknowledge 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.