Efficient search for relevance explanations using MAP-independence in Bayesian networks

Abstract

▪-independence is a novel concept concerned with explaining the (ir)relevance of intermediate nodes for maximum a posteriori (▪) computations in Bayesian networks. Building upon properties of ▪-independence, we introduce and experiment with methods for finding sets of relevant nodes using both an exhaustive and a heuristic approach. Our experiments show that these properties significantly speed up run time for both approaches. In addition, we link ▪-independence to defeasible reasoning, a type of reasoning that analyses how new evidence may invalidate an already established conclusion. Ways to present users with an explanation using ▪-independence are also suggested.