An Approximate Algorithm for Min-Based Possibilistic Networks

Abstract

Min-based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min-based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some 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 known as a hard problem. This paper proposes an approximate algorithm for inference in min-based possibilistic networks. More precisely, we adapt the well-known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP.