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NeurIPS 2021
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Proof Extraction for Logical Neural Networks

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Abstract

Automated Theorem Provers (ATPs) are widely used for the verification of logical statements. Explainability is one of the key advantages of ATPs: providing an expert readable proof path which shows the inference steps taken to conclude correctness. Conversely, Neuro-Symbolic Networks (NSNs) that perform theorem proving, do not have this capability. We propose a proof-tracing and filtering algorithm to provide explainable reasoning in the case of Logical Neural Networks(LNNs), a special type of Neural-Theorem Prover (NTP).

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NeurIPS 2021

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