Automated proof synthesis for propositional logic with deep neural networks

30 May 2018 Taro Sekiyama Kohei Suenaga

This work explores the application of deep learning, a machine learning technique that uses deep neural networks (DNN) in its core, to an automated theorem proving (ATP) problem. To this end, we construct a statistical model which quantifies the likelihood that a proof is indeed a correct one of a given proposition... (read more)

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