Proof Extraction for 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).
Cite
Text
Lebese et al. "Proof Extraction for Logical Neural Networks." NeurIPS 2021 Workshops: AIPLANS, 2021.Markdown
[Lebese et al. "Proof Extraction for Logical Neural Networks." NeurIPS 2021 Workshops: AIPLANS, 2021.](https://mlanthology.org/neuripsw/2021/lebese2021neuripsw-proof/)BibTeX
@inproceedings{lebese2021neuripsw-proof,
title = {{Proof Extraction for Logical Neural Networks}},
author = {Lebese, Thabang and Makondo, Ndivhuwo and Cornelio, Cristina and Khan, Naweed},
booktitle = {NeurIPS 2021 Workshops: AIPLANS},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/lebese2021neuripsw-proof/}
}