Restart Strategy Selection using Machine Learning Techniques
  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.
