主催: 人工知能学会
会議名: 第97回 人工知能基本問題研究会
回次: 97
開催地: 別府国際コンベンションセンター
開催日: 2014/03/22 - 2014/03/23
p. 18-
In this paper, we propose a high-level unsatisfiable core extraction technique for SAT translation approaches, in which a given instance is encoded into a propositional formula, and then the formula is solved by a SAT solver. If a model which satisfies the formula is found, then it is decoded into a solution of the instance. If the formula is unsatisfiable, that means the instance has no solution, generally. In the latter case, it is often required to find a cause of the unsuccessful result. To extract a high-level unsatisfiable core of the instance, the propositional UNSAT core should be decoded into the format of the instance, but the development of the decoding mechanism is not an easy work when the translation approach consists of multiple layers. We show a high-level unsatisfiable core extraction technique based on decoding process of a model that is equipped in SAT translation approaches.