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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Gogate, V., & Dechter, R. (2008). Approximate solution sampling (and counting) on AND/OR spaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5202 LNCS, pp. 534–538). https://doi.org/10.1007/978-3-540-85958-1_37
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