Prejudiced Information Fusion Using Belief Functions

Didier Dubois (1, 2) , Francis Faux (1, 3) , Henri Prade (1, 2)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
2 CNRS - Centre National de la Recherche Scientifique
3 INUC - Institut national universitaire Champollion [Albi]

G. Shafer views belief functions as the result of the fusion of elementary partially reliable testimonies from different sources. But any belief function cannot be seen as the combination of simple support functions representing such testimonies. Indeed the result of such a combination only yields a special kind of belief functions called separable. In 1995, Ph. Smets has indicated that any belief function can be seen as the combination of so-called generalized simple support functions. We propose a new interpretation of this result in terms of a pair of separable belief functions, one of them modelling testimonies while the other represents the idea of prejudice. The role of the latter is to weaken the weights of the focal sets of the former separable belief function. This bipolar view accounts for a form of resistance to accept the information supplied by the sources, which differs from the discounting of sources.

Mots clés: Belief functions

Domaines: Théorie de l'information [cs.IT]

Didier Dubois, Francis Faux, Henri Prade. Prejudiced Information Fusion Using Belief Functions. 5th International Conference on Belief Functions (BELIEF 2018), Sep 2018, Compiègne, France. pp.77-85, ⟨10.1007/978-3-319-99383-6_11⟩. ⟨hal-02181920⟩