Christian Bessiere, Remi Coletta, Frederic Koriche, Barry O’Sullivan
Constraint programming is a commonly used technology for solving complex combinatorial problems. However, users of this technology need significant expertise in order to model their problems appropriately. We propose a basis for addressing this problem: a new SAT-based version space algorithm for acquiring constraint networks from examples of solutions and non-solutions of a target problem. An important advantage of the algorithm is the ease with which domain-specific knowledge can be exploited.
Subjects: 15.2 Constraint Satisfaction; 12. Machine Learning and Discovery