Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Enhanced Iterative-Deepening Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Global Cut Framework for Removing Symmetries
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Constraints
AFIPS '67 (Fall) Proceedings of the November 14-16, 1967, fall joint computer conference
Domain filtering consistencies
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Exploiting past and future: pruning by inconsistent partial state dominance
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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In this paper, a state-based approach for the Constraint Satisfaction 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 recomputing the solutions of these subnetworks. Interestingly enough, the two approaches present some complementarity: different states can be pruned from the same partial instantiation 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 interchangeability). The obtained experimental results demonstrate the promising prospects of state-based search.