Abstract
In this paper, we describe a new algorithm for sampling solutions from a uniform distribution over the solutions of a constraint network. Our new algorithm improves upon the Sampling/Importance Resampling (SIR) component of our previous scheme of SampleSearch-SIR by taking advantage of the decomposition implied by the network’s AND/OR search space. We also describe how our new scheme can approximately count and lower bound the number of solutions of a constraint network. We demonstrate both theoretically and empirically that our new algorithm yields far better performance than competing approaches.
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Gogate, V., Dechter, R. (2008). Approximate Solution Sampling (and Counting) on AND/OR Spaces. In: Stuckey, P.J. (eds) Principles and Practice of Constraint Programming. CP 2008. Lecture Notes in Computer Science, vol 5202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85958-1_37
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DOI: https://doi.org/10.1007/978-3-540-85958-1_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85957-4
Online ISBN: 978-3-540-85958-1
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