Transposition Tables for Constraint Satisfaction

In this paper, a state-based approach for the Constraint Sat-isfaction Problem (CSP) is proposed. The key novelty is an original use of state memorization during search to prevent the exploration of similar subnetworks. Classical techniques to avoid the resurgence of previously encountered conflicts involve recording conflict sets. This contrasts with our state-based approach which records subnetworks – a snapshot of some selected domains – already explored. This knowledge is later used to either prune inconsistent states or avoid re-computing the solutions of these subnetworks. Interestingly enough, the two approaches present some complementarity: different states can be pruned from the same partial instan-tiation or conflict set, whereas different partial instantiations can lead to the same state that needs to be explored only once. Also, our proposed approach is able to dynamically break some kinds of symmetries (e.g. neighborhood interchange-ability). The obtained experimental results demonstrate the promising prospects of state-based search