Towards Finding Longer Proofs
Authors: Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, Cezary Kaliszyk, Josef Urban
Keywords: automated reasoning, reinforcement learning, reasoning by analogy
Abstract: We present a reinforcement learning (RL) based guidance system for automated theorem proving geared towards Finding Longer Proofs (FLoP). FLoP is a step towards learning to reason by analogy, reducing the dependence on large scale search in automated theorem provers. We use several simple, structured datasets with very long proofs to show that FLoP can successfully generalise a single training proof to a large class of related problems, implementing a simple form of analogical reasoning. On these benchmarks, FLoP is competitive with strong theorem provers despite using very limited search.