Cezary Kaliszyk, François Chollet, Christian Szegedy
TL;DR本文介绍了一种基于 Higher-Order Logic 证明的新数据集,旨在开发基于机器学习的定理证明策略并提出基于该数据集的各种机器学习任务,通过一些基于逻辑回归、卷积神经网络和循环神经网络的基准模型显示出将机器学习应用于 HOL 定理证明的前景。
Abstract
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 gener