Proof Extraction for Logical Neural NetworksDownload PDF

08 Oct 2021, 15:28 (modified: 27 Nov 2021, 11:43)AIPLANSReaders: Everyone
Keywords: neuro-symbolic learning, neural theorem proving, explainability, logic, learning, Neural program synthesis
TL;DR: Extracting human-readable proof steps for explaining the reasoning process of Logical Neural Networks
Abstract: Automated Theorem Provers (ATPs) are widely used for the verification of logicalstatements. Explainability is one of the key advantages of ATPs: providing anexpert readable proof path which shows the inference steps taken to concludecorrectness. Conversely, Neuro-Symbolic Networks (NSNs) that perform theoremproving, do not have this capability. We propose a proof-tracing and filteringalgorithm to provide explainable reasoning in the case of Logical Neural Networks(LNNs), a special type of Neural-Theorem Prover (NTP).
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