Possibility Theory in Information Fusion

  • Didier Dubois
  • Henri Prade
Part of the International Centre for Mechanical Sciences book series (CISM, volume 431)


Possibility theory and the body of aggregation operations from fuzzy set theory provide some tools to address the problem of merging information coming from several sources. Possibility theory is a representation framework that can model various kinds of information items: numbers, intervals, consonant random sets, special kind of probability families, as well as linguistic information, and uncertain formulae in logical settings. The possibilistic approach to fusion is general enough to encompass logical modes of combination (conjunctive and disjunctive) as well as fusion modes used in statistics. The choice of a fusion mode depends on assumptions on whether all sources are reliable or not, and can be based on conflict analysis. This general framework allows to import inconsistency handling methods, inherited from logic, into numerical fusion problems. Quantified, prioritized and weighted fusion rules are described, as well as fusion under a priori knowledge. It is shown that the possibilistic setting is compatible with the Bayesian approach to fusion, the main difference being the presupposed existence, or not, of prior knowledge. The approach applies to sensor fusion, aggregation of expert opinions as well as the merging of databases especially in case of poor, qualitative information.


Data Fusion Fusion Rule Information Fusion Belief Function Possibility Distribution 
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-Verlag Wien 2001

Authors and Affiliations

  • Didier Dubois
    • 1
  • Henri Prade
    • 1
  1. 1.IRIT-CNRSUniversité Paul SabatierToulouseFrance

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