Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
YIELDS: A Yet Improved Limited Discrepancy Search for CSPs
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Depth-bounded discrepancy search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Limited discrepancy beam search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Edge finding filtering algorithm for discrete cumulative resources in O(kn log n)
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Improved limited discrepancy search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Constraint propagation as the core of local search
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Optimal implementation of watched literals and more general techniques
Journal of Artificial Intelligence Research
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Harvey and Ginsberg's limited discrepancy search (LDS) is based on the assumption that costly heuristic mistakes are made early in the search process. Consequently, LDS repeatedly probes the state space, going against the heuristic (i.e., taking discrepancies) a specified number of times in all possible ways and attempts to take those discrepancies as early as possible. LDS was improved by Richard Korf, to become improved LDS (ILDS), but in doing so, discrepancies were taken as late as possible, going against the original assumption. Many subsequent algorithms have faithfully inherited Korf's interpretation of LDS, and take discrepancies late. This then raises the question: Should we take our discrepancies late or early? We repeat the original experiments performed by Harvey and Ginsberg and those by Korf in an attempt to answer this question. We also investigate the early stopping condition of the YIELDS algorithm, demonstrating that it is simple, elegant and efficient.