Margarita Razgon, Barry O'Sullivan, Gregory Provan
Over the past decade impressive advances have been made in solving Constraint Satisfaction Problems by using of randomization and restarts. In this paper we propose a new class of variable and value ordering heuristics based on learning from nogoods without storing them. We show empirically that these heuristics dramatically improve the performance of restarts-based constraint solving.
Subjects: 15.2 Constraint Satisfaction; 15.7 Search
Submitted: Feb 9, 2007