Prolog Technology Reinforcement Learning Prover
This work addresses the challenge of improving automated theorem proving efficiency for researchers in formal methods and AI, though it appears incremental as it builds on existing systems like leanCoP and rlCoP.
The researchers tackled the problem of guiding automated theorem proving in the connection calculus by developing a reinforcement learning toolkit based on a Prolog prover called plCoP, which integrates learning-guided Monte-Carlo Tree Search and is evaluated on two benchmarks with extensions for reduction steps and rewrite steps.
We present a reinforcement learning toolkit for experiments with guiding automated theorem proving in the connection calculus. The core of the toolkit is a compact and easy to extend Prolog-based automated theorem prover called plCoP. plCoP builds on the leanCoP Prolog implementation and adds learning-guided Monte-Carlo Tree Search as done in the rlCoP system. Other components include a Python interface to plCoP and machine learners, and an external proof checker that verifies the validity of plCoP proofs. The toolkit is evaluated on two benchmarks and we demonstrate its extendability by two additions: (1) guidance is extended to reduction steps and (2) the standard leanCoP calculus is extended with rewrite steps and their learned guidance. We argue that the Prolog setting is suitable for combining statistical and symbolic learning methods. The complete toolkit is publicly released.