Abstract
Max Restricted Path Consistency (maxRPC) is a local consistency for binary constraints that can achieve considerably stronger pruning than arc consistency. However, existing maxRPC algorithms suffer from overheads and redundancies as they can repeatedly perform many constraint checks without triggering any value deletions. In this paper we propose techniques that can boost the performance of maxRPC algorithms. These include the combined use of two data structures to avoid many redundant constraint checks, and heuristics for the efficient ordering and execution of certain operations. Based on these, we propose two closely related maxRPC algorithms. The first one has optimal O(end 3) time complexity, displays good performance when used stand-alone, but is expensive to apply during search. The second one has O(en 2 d 4) time complexity, but a restricted version with O(end 4) complexity can be very efficient when used during search. Both algorithms have O(ed) space complexity when used stand-alone. However, the first algorithm has O(end) space complexity when used during search, while the second retains the O(ed) complexity. Experimental results demonstrate that the resulting methods constantly outperform previous algorithms for maxRPC, often by large margins, and constitute a more than viable alternative to arc consistency.
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References
Balafoutis, T., Stergiou, K.: Exploiting constraint weights for revision ordering in Arc Consistency Algorithms. In: ECAI 2008 Workshop on Modeling and Solving Problems with Constraints (2008)
Bessière, C., Régin, J.C., Yap, R., Zhang, Y.: An Optimal Coarse-grained Arc Consistency Algorithm. Artificial Intelligence 165(2), 165–185 (2005)
Boussemart, F., Hemery, F., Lecoutre, C.: Revision ordering heuristics for the Constraint Satisfaction Problem. In: CP-2004 Workshop on Constraint Propagation (2004)
Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proceedings of ECAI-2004 (2004)
Debruyne, R., Bessière, C.: From restricted path consistency to max-restricted path consistency. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 312–326. Springer, Heidelberg (1997)
Debruyne, R., Bessière, C.: Domain Filtering Consistencies. JAIR 14, 205–230 (2001)
Grandoni, F., Italiano, G.: Improved Algorithms for Max-Restricted Path Consistency. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 858–862. Springer, Heidelberg (2003)
Lecoutre, C., Hemery, F.: A study of residual supports in arc cosistency. In: Proceedings of IJCAI 2007, pp. 125–130 (2007)
Likitvivatanavong, C., Zhang, Y., Bowen, J., Shannon, S., Freuder, E.: Arc Consistency during Search. In: Proceedings of IJCAI 2007, pp. 137–142 (2007)
Vion, J., Debruyne, R.: Light Algorithms for Maintaining Max-RPC During Search. In: Proceedings of SARA 2009 (2009)
Wallace, R., Freuder, E.: Ordering heuristics for arc consistency algorithms. In: AI/GI/VI, Vancouver, British Columbia, Canada, pp. 163–169 (1992)
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Balafoutis, T., Paparrizou, A., Stergiou, K., Walsh, T. (2010). Improving the Performance of maxRPC. In: Cohen, D. (eds) Principles and Practice of Constraint Programming – CP 2010. CP 2010. Lecture Notes in Computer Science, vol 6308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15396-9_9
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DOI: https://doi.org/10.1007/978-3-642-15396-9_9
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