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arXiv · 2006.11259

Learning to Prove from Synthetic Theorems

Eser Aygün·Zafarali Ahmed·Ankit Anand·Vlad Firoiu·Xavier Glorot·Laurent Orseau·Doina Precup·Shibl Mourad
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Abstract

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training with synthetic theorems, generated from a set of axioms. We show that such theorems can be used to train an automated prover and that the learned prover transfers successfully to human-generated theorems. We demonstrate that a prover trained exclusively on synthetic theorems can solve a substantial fraction of problems in TPTP, a benchmark dataset that is used to compare state-of-the-art heuristic provers. Our approach outperforms a model trained on human-generated problems in most axiom sets, thereby showing the promise of using synthetic data for this task.

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Eser Aygün, Zafarali Ahmed, Ankit Anand, Vlad Firoiu, Xavier Glorot, Laurent Orseau, Doina Precup, Shibl Mourad. 2020-06-19. Learning to Prove from Synthetic Theorems. https://arxiv.org/abs/2006.11259

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