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arXiv 2004.06997cs.LOcs.LG
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Prolog Technology Reinforcement Learning Prover

Alfréd Rényi Institute of Mathematics(阿尔弗雷德·雷尼数学研究所);Eötvös Loránd University(厄特沃什·罗兰大学)

Zsolt Zombori, Josef Urban, Chad E. Brown

英文摘要:

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.

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