Hierarchical beam search for solving most relevant explanation in Bayesian networks

Abstract

Most Relevant Explanation (MRE) is an inference problem in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given evidence. It has been shown in recent literature that it addresses the overspecification problem of existing methods, such as MPE and MAP. In this paper, we propose a novel hierarchical beam search algorithm for solving MRE. The main idea is to use a second-level beam to limit the number of successors generated by the same parent so as to limit the similarity between the solutions in the first-level beam and result in a more diversified population. Three pruning criteria are also introduced to achieve further diversity. Empirical results show that the new algorithm outperforms local search and regular beam search.