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.
