Restart Strategy Selection using Machine Learning Techniques

Details Restart Strategy Selection using Machine Learning Techniques Restart Strategy Selection using Machine Learning Techniques

2009-07-29

arxiv.org/abs/0907.5032v1

Computer Science

Artificial Intelligence

14 pages, 4 figures

Scientific paper

Restart strategies are an important factor in the performance of conflict-driven Davis Putnam style SAT solvers. Selecting a good restart strategy for a problem instance can enhance the performance of a solver. Inspired by recent success applying machine learning techniques to predict the runtime of SAT solvers, we present a method which uses machine learning to boost solver performance through a smart selection of the restart strategy. Based on easy to compute features, we train both a satisfiability classifier and runtime models. We use these models to choose between restart strategies. We present experimental results comparing this technique with the most commonly used restart strategies. Our results demonstrate that machine learning is effective in improving solver performance.

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