Generating All Abductive Explanations for Queries on Propositional Horn Theories

Abstract. Abduction is a fundamental mode of reasoning, which has taken on increasing importance in Artificial Intelligence (AI) and related disciplines. Computing abductive explanations is an important problem, and there is a growing literature on this subject. We contribute to this endeavor by presenting new results on computing multiple resp. all of the possibly exponentially many explanations of an abductive query from a propositional Horn theory represented by a Horn CNF. Here the issues are whether a few explanations can be generated efficiently and, in case of all explanations, whether the computation is possible in polynomial total time (or output-polynomial time), i.e., in time polynomial in the combined size of the input and the output. We explore these issues for queries in CNF and important restrictions thereof. Among the results, we show that computing all explanations for a negative query literal from a Horn CNF is not feasible in polynomial total time unless P = NP, wh...

• Type: Conference
• Year: 2003
• Where: CSL
• Authors: Thomas Eiter, Kazuhisa Makino
• Keywords: Abductive Explanations, CSL 2003, Horn CNF, Polynomial Total Time