ON FINDING MINIMALLY UNSATISFIABLE CORES OF CSPs
Éric Grégoire, Bertrand Mazure, Cédric Piette · International Journal of Artificial Intelligence Tools · 2008
When a Constraint Satisfaction Problem (CSP) admits no solution, it can be useful to pinpoint which constraints are actually contradicting one another and make the problem infeasible. In this paper, a recent heuristic-based approach to compute infeasible minimal subparts of discrete CSPs, also called Minimally Unsatisfiable Cores (MUCs), is improved. The approach is based on the heuristic exploitation of the number of times each constraint has been falsified during previous failed search steps. It appears to enhance the performance of the initial technique, which was the most efficient one until now.