Learning to Reason

Abstract

Theorem proving formalizes the notion of deductive reasoning, while machine learning formalizes the notion of inductive reasoning. In this thesis, we present an overview of the current state of machine learning guided first-order automated theorem proving systems and outline a novel high-level modular object-oriented framework for combining arbitrary machine learning models with arbitrary proof calculi. Additionally, we present an example implementation in the aforementioned framework taking a novel approach to combining graph neural networks with the first-order connection calculus, generating a new Python implementation of the leanCoP theorem prover as a by-product