On analysis of unicity of Jeffrey's rule of conditioning in a probabilistic framework

Conditioning is an important task for designing intelligent systems in artificial intelligence. This paper addresses an issue related to the possibilistic counterparts of Jeffrey’s rule of conditioning. More precisely, it addresses the existence and unicity of solutions computed using the possibilistic counterparts of the socalled kinematics properties underlying Jeffrey’s rule of conditioning. We first point out that like the probabilistic framework, in the quantitative possibilistic setting, there exists a unique solution for revising a possibility distribution given uncertainty bearing on a set of exhaustive and mutually exclusive events. However, in the qualitative possibilistic framework, the situation is different. In particular, the application of the kinematics principle does not guarantee the existence of a solution. We provide precise conditions where the unicity of the revised possibility distribution exists. On analysis of the unicity of Jeffrey’s rule of conditioning in a possibilistic framework Salem Benferhat, Karim Tabia, Karima Sedki CRIL CNRS UMR 8188 Université d’Artois {benferhat,tabia,sedki}@cril.univ-artois.fr