Feature-Based Learning of Search-Guiding Heuristics for Theorem Proving

b b b b b b b b b b b b b b b b b b b Abstract Automated reasoning or theorem proving essentially amounts to solving search problems. Despite signiicant progress in recent years theorem provers still have many shortcomings. The use of machine-learning techniques is acknowledged as promising, but diicult to apply in the area of theorem proving. We propose here to learn search-guiding heuristics by employing features in a simple, yet eeective manner. Features are used to adapt a heuristic to a solved source problem. The adapted heuristic can then be utilized prootably for solving related target problems. Experiments have demonstrated that the approach allows a theorem prover to prove hard problems that were out of reach before.