Approximate inference in qualitative possibilistic networks

Min-based (or qualitative) possibilistic networks appear to be important tools to efficiently and compactly represent and analyze uncertain information. Inference is the crucial task which consists in propagating information through the network structure. Exact inference calculates posterior possibilistic distributions given an observed evidence in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is considered as a hard problem. This paper proposes an approximate algorithm for inference in min-based possibilistic networks. More precisely, we apply the principle of a well-known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. In experimental results, we focus on convergence study of LBP and we show that the proposed algorithm gives remarkably good results that are better than LBP applied on quantitative possibilistic networks case [1].