Finding Explanations: an Empirical Evaluation of Abductive Diagnosis Algorithms

Abductive inference provides consistent explanations for observable effects and has been of special interest in the context of diagnosis. The abduction problem is in general NP-hard, thus, there is a high motivation to derive solutions efficiently for practical instances. In this paper, we focus on propositional abduction in the framework of model-based diagnosis. We review four algorithms to compute explanations: one employs an ATMS to derive diagnoses and the others are con ict-directed methods based on an unsatisfiable reformulation of the abductive system description. In an empirical evaluation we compare the different approaches on practical examples. Our experiments indicate that the ATMS provides the best performance results for the majority of problems.