Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Generating random solutions for constraint satisfaction problems
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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.