AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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Exploring the Duality in Conflict-Directed Model-Based Diagnosis
Roni Tzvi Stern, Meir Kalech, Alexander Feldman, Gregory Provan

Last modified: 2012-07-14


A model-based diagnosis problem occurs when an observation is inconsistent with the assumption that the diagnosed system is not faulty. The task of a diagnosis engine is to compute diagnoses, which are assumptions on the health of components in the diagnosed system that explain the observation. In this paper, we extend Reiter's well-known theory of diagnosis by exploiting the duality of the relation between conflicts and diagnoses. This duality means that a diagnosis is a hitting set of conflicts, but a conflict is also a hitting set of diagnoses. We use this property to interleave the search for diagnoses and conflicts: a set of conflicts can guide the search for diagnosis, and the computed diagnoses can guide the search for more conflicts. We provide the formal basis for this dual conflict-diagnosis relation, and propose a novel diagnosis algorithm that exploits this duality. Experimental results show that the new algorithm is able to find a minimal cardinality diagnosis faster than the well-known Conflict-Directed A*.


Artificial Intelligence, Model-based diagnosis, reasoning

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