Generating random solutions for constraint satisfaction problems
Eighteenth national conference on Artificial intelligence
AND/OR search spaces for graphical models
Artificial Intelligence
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Approximate counting by sampling the backtrack-free search space
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A new algorithm for sampling CSP solutions uniformly at random
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Importance sampling-based estimation over AND/OR search spaces for graphical models
Artificial Intelligence
Algorithms for generating ordered solutions for explicit and/or structures
Journal of Artificial Intelligence Research
Algorithms for generating ordered solutions for explicit AND/OR structures: extended abstract
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.