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.
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Castro, C., Monfroy, E., Figueroa, C., Meneses, R. (2005). An Approach for Dynamic Split Strategies in Constraint Solving. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_17
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DOI: https://doi.org/10.1007/11579427_17
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