Handling bipolar knowledge with imprecise probabilities

Sébastien Destercke (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

Information is said to be bipolar when it has a positive and a negative part. The problem of representing and processing such bipolar information has recently received a lot of attention in uncertainty theories. In this paper, we are concerned with the representation of asymmetric bipolarity, i.e., with situations where positive and negative information are unrelated and processed in parallel. In this latter case, positive information consists of observations of experiment results, showing what values are possible, whereas negative information consists of constraints (e.g., provided by an expert), restricting the range of possible variable values. Up to now, there are no proposition as to how such bipolar information can be treated in the framework of imprecise probability theory, i.e., when information is represented by convex sets of probabilities. In this paper, we propose the basis of such a framework and provide some illustrative examples.

Mots clés: BIPOLARITY; CREDAL SETS; INFORMATION FUSION; UNCERTAINTY REPRESENTATION; THEORIE DES PROBABILITES IMPRECISES; REPRESENTATION DES INCERTITUDES

Mots clés: INFORMATION BIPOLAIRE; BIPOLARITE ASYMETRIQUE; VALEURS DES VARIABLES

Domaines: Intelligence artificielle [cs.AI]

Sébastien Destercke. Handling bipolar knowledge with imprecise probabilities. International Journal of Intelligent Systems, 2012, 26 (5), pp.426-443. ⟨10.1002/int.20475⟩. ⟨hal-00652236⟩