Finding dissimilar explanations in Bayesian networks: Complexity results

TL;DR

This paper investigates the computational complexity of identifying multiple diverse explanations in Bayesian networks, revealing that finding dissimilar plausible explanations is as hard as finding the most probable one.

Contribution

It establishes the complexity of finding multiple, structurally dissimilar explanations in Bayesian networks, extending understanding of inference problems in probabilistic models.

Findings

Finding dissimilar explanations is computationally as hard as finding the best explanation.

The problem is intractable in general, indicating high computational difficulty.

Implications for applications requiring diverse explanations in AI systems.

Abstract

Finding the most probable explanation for observed variables in a Bayesian network is a notoriously intractable problem, particularly if there are hidden variables in the network. In this paper we examine the complexity of a related problem, that is, the problem of finding a set of sufficiently dissimilar, yet all plausible, explanations. Applications of this problem are, e.g., in search query results (you won't want 10 results that all link to the same website) or in decision support systems. We show that the problem of finding a 'good enough' explanation that differs in structure from the best explanation is at least as hard as finding the best explanation itself.