• DocumentCode
    1679576
  • Title

    Revision Rules in the Theory of Evidence

  • Author

    Ma, Jianbing ; Liu, Weiru ; Dubois, Didier ; Prade, Henri

  • Author_Institution
    Queen´´s Univ. of Belfast, Belfast, UK
  • Volume
    1
  • fYear
    2010
  • Firstpage
    295
  • Lastpage
    302
  • Abstract
    Combination rules proposed so far in the Dempster-Shafer theory of evidence, especially Dempster rule, rely on a basic assumption, that is, pieces of evidence being combined are considered to be on a par, i.e. play the same role. When a source of evidence is less reliable than another, it is possible to discount it and then a symmetric combination operation is still used. In the case of revision, the idea is to let prior knowledge of an agent be altered by some input information. The change problem is thus intrinsically asymmetric. Assuming the input information is reliable, it should be retained whilst the prior information should be changed minimally to that effect. Although belief revision is already an important subfield of artificial intelligence, so far, it has been little addressed in evidence theory. In this paper, we define the notion of revision for the theory of evidence and propose several different revision rules, called the inner and outer revisions, and a modified adaptive outer revision, which better corresponds to the idea of revision. Properties of these revision rules are also investigated.
  • Keywords
    belief maintenance; inference mechanisms; uncertainty handling; Dempster-Shafer theory; artificial intelligence; belief revision; combination rule; theory of evidence; Bayesian methods; Bipartite graph; Electronic mail; Kinematics; Merging; Partitioning algorithms; Reliability; DS theory; Jeffrey´s rule; belief revision; combination rules; evidence theory; inner revision; outer revision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on
  • Conference_Location
    Arras
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4244-8817-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2010.50
  • Filename
    5670049