Computational complexity and approximization methods of most relevant explanation
نویسندگان
چکیده
Most Relevant Explanation (MRE) is a new approach to generating explanations for given evidence in Bayesian networks. MRE has a solution space containing all the partial instantiations of target variables and is extremely hard to solve. We show in this paper that the decision problem of MRE is NP -complete. For large Bayesian networks, approximate methods may be the only feasible solutions. We observe that the solution space of MRE has a special lattice structure that connects all the potential solutions together. The connectivity motivates us to develop several efficient local search methods for solving MRE. Empirical results show that these methods can efficiently find the optimal MRE solutions for majority of the test cases in our experiments.
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