Abstract
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.
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References
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© 2006 Springer-Verlag Berlin Heidelberg
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Gogate, V., Dechter, R. (2006). A New Algorithm for Sampling CSP Solutions Uniformly at Random. In: Benhamou, F. (eds) Principles and Practice of Constraint Programming - CP 2006. CP 2006. Lecture Notes in Computer Science, vol 4204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889205_56
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DOI: https://doi.org/10.1007/11889205_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46267-5
Online ISBN: 978-3-540-46268-2
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