Abstract
In constraint programming, a priori choices statically determine strategies that are crucial for resolution performances. However, the effect of strategies is generally unpredictable. We propose to dynamically change strategies showing bad performances. When this is not enough to improve resolution, we introduce some meta-backtracks. Our goal is to get good performances without the know-how of experts. Some first experimental results show the effectiveness of our approach. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Castro, C., Monfroy, E., Figueroa, C., & Meneses, R. (2005). An approach for dynamic split strategies in constraint solving. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3789 LNAI, pp. 162–174). https://doi.org/10.1007/11579427_17
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