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• W2896461135 abstract "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. Based on this model, we give a proof-synthesis procedure that searches for a proof in the order of the likelihood. This procedure uses an estimator of the likelihood of an inference rule being applied at each step of a proof. As an implementation of the estimator, we propose a proposition-to-proof architecture, which is a DNN tailored to the automated proof synthesis problem. To empirically demonstrate its usefulness, we apply our model to synthesize proofs of the minimal propositional logic. We train the proposition-to-proof model using a training dataset of proposition–proof pairs. The evaluation against a benchmark set shows the very high accuracy and an improvement to the recent work of neural proof synthesis." @default.
• W2896461135 created "2018-10-26" @default.
• W2896461135 creator A5018002187 @default.
• W2896461135 creator A5064121410 @default.
• W2896461135 date "2018-01-01" @default.
• W2896461135 modified "2023-09-27" @default.
• W2896461135 title "Automated Proof Synthesis for the Minimal Propositional Logic with Deep Neural Networks" @default.
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• W2896461135 doi "https://doi.org/10.1007/978-3-030-02768-1_17" @default.
• W2896461135 hasPublicationYear "2018" @default.
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