Possibilistic logic: A theoretical framework for multiple source information fusion

Souhila Kaci (1) , Salem Benferhat (1) , Didier Dubois (1) , Henri Prade (1)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

The problem of merging or combining multiple sources information is central in many information processing areas such as databases integrating problems, expert opinion pooling, preference aggregation, etc. Possibilistic logic offers a qualitative framework for representing pieces of information associated with levels of uncertainty or priority. This paper discusses the fusion of multiple sources information in this setting. Different classes of merging operators are considered, at the semantic and the syntactic level, including conjunctive, disjunctive, reinforcement, adaptive and averaging operators. This framework appears to be the syntactic counterpart of the pointwise aggregation of possibility distributions or fuzzy sets.

Domaines: Intelligence artificielle [cs.AI]

Souhila Kaci, Salem Benferhat, Didier Dubois, Henri Prade. Possibilistic logic: A theoretical framework for multiple source information fusion. Leon Reznik; Vladik Kreinovich. Soft Computing in Measurement and Information Acquisition, 127, Springer-Verlag, pp.68-89, 2003, Studies in Fuzziness and Soft Computing book series (STUDFUZZ), 978-3-540-36216-6. ⟨10.1007/978-3-540-36216-6_6⟩. ⟨hal-03373363⟩