Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
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Artificial Intelligence
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Domain-splitting generalized nogoods from restarts
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
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INFORMS Journal on Computing
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CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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In this paper, nogood recording is investigated within the randomization and restart framework. Our goal is to avoid the same situations to occur from one run to the next one. More precisely, no-goods are recorded when the current cutoff value is reached, i.e. before restarting the search algorithm. Such a set of nogoods is extracted from the last branch of the current search tree. Interestingly, the number of nogoods recorded before each new run is bounded by the length of the last branch of the search tree. As a consequence, the total number of recorded nogoods is polynomial in the number of restarts. Experiments over a wide range of CSP instances demonstrate the effectiveness of our approach.