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arXiv · 1905.10501

Learning to Reason in Large Theories without Imitation

Kshitij Bansal·Christian Szegedy·Markus N. Rabe·Sarah M. Loos·Viktor Toman
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Abstract

In this paper, we demonstrate how to do automated theorem proving in the presence of a large knowledge base of potential premises without learning from human proofs. We suggest an exploration mechanism that mixes in additional premises selected by a tf-idf (term frequency-inverse document frequency) based lookup in a deep reinforcement learning scenario. This helps with exploring and learning which premises are relevant for proving a new theorem. Our experiments show that the theorem prover trained with this exploration mechanism outperforms provers that are trained only on human proofs. It approaches the performance of a prover trained by a combination of imitation and reinforcement learning. We perform multiple experiments to understand the importance of the underlying assumptions that make our exploration approach work, thus explaining our design choices.

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BibTeXRIS

Kshitij Bansal, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Viktor Toman. 2019-05-25. Learning to Reason in Large Theories without Imitation. https://arxiv.org/abs/1905.10501

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