Asín, R.J.; Aloysius, M.; Nieuwenhuis, R. AI communications: the european journal of artificial intelligence Vol. 29, num. 1, p. 205-209 DOI: 10.3233/AIC-150684 Data de publicació: 2016 Article en revista
Conflict-Driven Clause Learning (CDCL) SAT solvers can automatically solve very large real-world problems. IntSat is a new technique extending CDCL to Integer Linear Programming (ILP). For some conflicts, IntSat generates no strong enough ILP constraint, and the backjump has to be done based on the conflicting set of bounds. The techniques given in this article precisely analyze how and when that set can be translated into the required new ILP constraint. Moreover, this can be done efficiently. This obviously strengthens learning and significantly improves the performance of IntSat (as confirmed by our experiments). We also briefly discuss extensions and other applications.