A comparison of decision alaysis and expert rules for sequential diagnosis
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A general framework for generating multivariate explanations in Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Abductive inference in bayesian networks: finding a partition of the explanation space
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Most Relevant Explanation: computational complexity and approximation methods
Annals of Mathematics and Artificial Intelligence
Most relevant explanation in Bayesian networks
Journal of Artificial Intelligence Research
Learning optimal bayesian networks: a shortest path perspective
Journal of Artificial Intelligence Research
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Most Relevant Explanation (MRE) is a method for finding multivariate explanations for given evidence in Bayesian networks [12]. This paper studies the theoretical properties of MRE and develops an algorithm for finding multiple top MRE solutions. Our study shows that MRE relies on an implicit soft relevance measure in automatically identifying the most relevant target variables and pruning less relevant variables from an explanation. The soft measure also enables MRE to capture the intuitive phenomenon of explaining away encoded in Bayesian networks. Furthermore, our study shows that the solution space of MRE has a special lattice structure which yields interesting dominance relations among the solutions. A K-MRE algorithm based on these dominance relations is developed for generating a set of top solutions that are more representative. Our empirical results show that MRE methods are promising approaches for explanation in Bayesian networks.