The paper presents a method for generating solutions of a constraint satisfaction problem (CSP) uniformly at random. Our method relies on expressing the constraint network as a uniform probability distribution over its solutions and then sampling from the distribution using state-of-the-art probabilistic sampling schemes. To speed up the rate at which random solutions are generated, we augment our sampling schemes with pruning techniques used successfully in constraint satisfaction search algorithms such as conflict-directed back-jumping and no-good learning. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Gogate, V., & Dechter, R. (2006). A new algorithm for sampling CSP solutions uniformly at random. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4204 LNCS, pp. 711–715). Springer Verlag. https://doi.org/10.1007/11889205_56
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