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HolStep: a Machine Learning Dataset for Higher-Order Logic Theorem Proving

Cezary Kaliszyk
Christian Szegedy
ICLR 2017 (2017) (to appear)


Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of baseline deep learning models suited for the tasks (including convolutional neural networks and recurrent neural networks). The results of our baseline models shows the promise of applying deep learning to HOL theorem proving.

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