Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics

Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516 and Kammueller, Florian ORCID: https://orcid.org/0000-0001-5839-5488 (2020) Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics. Advances in Information and Communication Networks: Proceedings of the 2019 Future of Information and Communication Conference (FICC), Volume 2. In: FICC 2019: Future of Information and Communications Conference, 14-15 Mar 2019, San Francisco, USA. ISBN 9783030123840. ISSN 2367-3370 [Conference or Workshop Item] (doi:10.1007/978-3-030-12385-7_22)

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Abstract

We outline a general strategy for the application of edit- distance based kernels to NP Theorem Proving in order to allow for polynomial-time machine learning of proof heuristics without the loss of sequential structural information associated with conventional feature- based machine learning. We provide a general short introduction to logic and proof considering a few important complexity results to set the scene and highlight the relevance of our findings.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Paper published as:
Windridge D., Kammüller F. (2020) Edit Distance Kernelization of NP Theorem Proving For Polynomial-Time Machine Learning of Proof Heuristics. In: Arai K., Bhatia R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 25283
Notes on copyright: This is a post-peer-review, pre-copyedit version of an article published in Advances in Information and Communication Networks: Proceedings of the 2019 Future of Information and Communication Conference (FICC), Volume 2. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-12385-7_22
Useful Links:
Depositing User: Florian Kammueller
Date Deposited: 03 Oct 2018 15:49
Last Modified: 17 Jun 2021 20:06
URI: https://eprints.mdx.ac.uk/id/eprint/25283

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