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Prolog Technology Reinforcement Learning Prover: (System Description)
We present a reinforcement learning toolkit for experiments with guiding automated theorem proving in the connection calculus. The core of the toolkit is a compact and easy to extend Prolog-based automated theorem prover called plCoP. plCoP builds on the leanCoP Prolog implementation and adds learni...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324016/ http://dx.doi.org/10.1007/978-3-030-51054-1_33 |
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author | Zombori, Zsolt Urban, Josef Brown, Chad E. |
author_facet | Zombori, Zsolt Urban, Josef Brown, Chad E. |
author_sort | Zombori, Zsolt |
collection | PubMed |
description | We present a reinforcement learning toolkit for experiments with guiding automated theorem proving in the connection calculus. The core of the toolkit is a compact and easy to extend Prolog-based automated theorem prover called plCoP. plCoP builds on the leanCoP Prolog implementation and adds learning-guided Monte-Carlo Tree Search as done in the rlCoP system. Other components include a Python interface to plCoP and machine learners, and an external proof checker that verifies the validity of plCoP proofs. The toolkit is evaluated on two benchmarks and we demonstrate its extendability by two additions: (1) guidance is extended to reduction steps and (2) the standard leanCoP calculus is extended with rewrite steps and their learned guidance. We argue that the Prolog setting is suitable for combining statistical and symbolic learning methods. The complete toolkit is publicly released. |
format | Online Article Text |
id | pubmed-7324016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73240162020-06-30 Prolog Technology Reinforcement Learning Prover: (System Description) Zombori, Zsolt Urban, Josef Brown, Chad E. Automated Reasoning Article We present a reinforcement learning toolkit for experiments with guiding automated theorem proving in the connection calculus. The core of the toolkit is a compact and easy to extend Prolog-based automated theorem prover called plCoP. plCoP builds on the leanCoP Prolog implementation and adds learning-guided Monte-Carlo Tree Search as done in the rlCoP system. Other components include a Python interface to plCoP and machine learners, and an external proof checker that verifies the validity of plCoP proofs. The toolkit is evaluated on two benchmarks and we demonstrate its extendability by two additions: (1) guidance is extended to reduction steps and (2) the standard leanCoP calculus is extended with rewrite steps and their learned guidance. We argue that the Prolog setting is suitable for combining statistical and symbolic learning methods. The complete toolkit is publicly released. 2020-06-06 /pmc/articles/PMC7324016/ http://dx.doi.org/10.1007/978-3-030-51054-1_33 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Zombori, Zsolt Urban, Josef Brown, Chad E. Prolog Technology Reinforcement Learning Prover: (System Description) |
title | Prolog Technology Reinforcement Learning Prover: (System Description) |
title_full | Prolog Technology Reinforcement Learning Prover: (System Description) |
title_fullStr | Prolog Technology Reinforcement Learning Prover: (System Description) |
title_full_unstemmed | Prolog Technology Reinforcement Learning Prover: (System Description) |
title_short | Prolog Technology Reinforcement Learning Prover: (System Description) |
title_sort | prolog technology reinforcement learning prover: (system description) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324016/ http://dx.doi.org/10.1007/978-3-030-51054-1_33 |
work_keys_str_mv | AT zomborizsolt prologtechnologyreinforcementlearningproversystemdescription AT urbanjosef prologtechnologyreinforcementlearningproversystemdescription AT brownchade prologtechnologyreinforcementlearningproversystemdescription |