Learning in local search

Gilles Audemard (1) , Jean-Marie Lagniez (1) , Bertrand Mazure (1) , Lakhdar Saïs (1)

1 CRIL - Centre de Recherche en Informatique de Lens

In this paper a learning based local search approach for propositional satisfiability is presented. It is based on an original adaptation of the conflict driven clause learning (CDCL) scheme to local search. First an extended implica- tion graph for complete assignments of the set of variables is proposed. Secondly, a unit propagation based technique for building and using such implication graph is designed. Finally, we show how this new learning scheme can be in- tegrated to the state-of-the-art local search solver WSAT. Interestingly enough, the obtained local search approach is able to prove unsatisfiability. Experimental results show very good performances on many classes of SAT instances from the last SAT competitions.

Domaines: Intelligence artificielle [cs.AI]

Gilles Audemard, Jean-Marie Lagniez, Bertrand Mazure, Lakhdar Saïs. Learning in local search. 21st International Conference on Tools with Artificial Intelligence (ICTAI'09), 2009, Newark, United States. pp.417-424. ⟨hal-00865365⟩