Extracting MUCs from Constraint Networks

Abstract. We address the problem of extracting Minimal Unsatisfiable Cores (MUCs) from constraint networks. This computationally hard problem has a practical interest in many application domains such as configuration, planning, diagnosis, etc. Indeed, identifying one or several disjoint MUCs can help circumscribe different sources of inconsistency in order to repair a system. In this paper, we propose an original approach that involves performing successive runs of a complete backtracking search, using constraint weighting, in order to surround an inconsistent part of a network, before identifying all transition constraints belonging to a MUC using a dichotomic process. We show the effectiveness of this approach, both theoretically and experimentally.

• Type: Conference
• Year: 2006
• Where: ECAI
• Authors: Fred Hemery, Christophe Lecoutre, Lakhdar Sais, Frédéric Boussemart
• Keywords: Artificial Intelligence, Constraint Networks, ECAI 2006, Hard Problem, Minimal Unsatisfiable Cores