AvatarSAT: An Auto-tuning Boolean SAT Solver

We present AvatarSAT, a SAT solver that uses machine-learning classifiers to automatically tune the heuristics of an off-the-shelf SAT solver on a per-instance basis. The classifiers use features of both the input and conflict clauses to select parameter settings for the solver's tunable heuristics. On a randomly selected set of SAT problems chosen from the 2007 and 2008 SAT competitions, AvatarSAT is, on average, over two times faster than MiniSAT based on the geometric mean speedup measure and 50% faster based on the arithmeticmean speedup measure. Moreover, AvatarSAT is hundreds to thousands of times faster than MiniSAT on many hard SAT instances and is never more than twenty times slower than MiniSAT on any SAT instance

Metareasoning about propagators for constraint satisfaction

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances