Partial Label Learning for Automated Theorem Proving

TL;DR

This paper introduces a novel approach by framing Automated Theorem Proving as Partial Label Learning, providing a theoretical basis and demonstrating improved performance with existing methods.

Contribution

It bridges the fields of Automated Theorem Proving and Partial Label Learning, offering a new theoretical framework and practical validation.

Findings

Methods from Partial Label Learning improve theorem prover performance

First theoretical framework connecting these fields

Demonstrated using the plCoP theorem prover

Abstract

We formulate learning guided Automated Theorem Proving as Partial Label Learning, building the first bridge across these fields of research and providing a theoretical framework for dealing with alternative proofs during learning. We use the plCoP theorem prover to demonstrate that methods from the Partial Label Learning literature tend to increase the performance of learning assisted theorem provers.