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Machine Learning Guidance for Connection Tableaux.

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Title: Machine Learning Guidance for Connection Tableaux.
Authors: Färber, Michael1, Kaliszyk, Cezary1 cezary.kaliszyk@uibk.ac.at, Urban, Josef2
Source: Journal of Automated Reasoning. Feb2021, Vol. 65 Issue 2, p287-320. 34p.
Subjects: Monte Carlo method, Machine learning, Logic programming, Artificial intelligence, Matrices (Mathematics)
Abstract: Connection calculi allow for very compact implementations of goal-directed proof search. We give an overview of our work related to connection tableaux calculi: first, we show optimised functional implementations of connection tableaux proof search, including a consistent Skolemisation procedure for machine learning. Then, we show two guidance methods based on machine learning, namely reordering of proof steps with Naive Bayesian probabilities, and expansion of a proof search tree with Monte Carlo Tree Search. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Automated Reasoning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Machine Learning Guidance for Connection Tableaux.
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  Data: <searchLink fieldCode="AR" term="%22Färber%2C+Michael%22">Färber, Michael</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kaliszyk%2C+Cezary%22">Kaliszyk, Cezary</searchLink><relatesTo>1</relatesTo><i> cezary.kaliszyk@uibk.ac.at</i><br /><searchLink fieldCode="AR" term="%22Urban%2C+Josef%22">Urban, Josef</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Automated+Reasoning%22">Journal of Automated Reasoning</searchLink>. Feb2021, Vol. 65 Issue 2, p287-320. 34p.
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  Data: <searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+programming%22">Logic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Matrices+%28Mathematics%29%22">Matrices (Mathematics)</searchLink>
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  Data: Connection calculi allow for very compact implementations of goal-directed proof search. We give an overview of our work related to connection tableaux calculi: first, we show optimised functional implementations of connection tableaux proof search, including a consistent Skolemisation procedure for machine learning. Then, we show two guidance methods based on machine learning, namely reordering of proof steps with Naive Bayesian probabilities, and expansion of a proof search tree with Monte Carlo Tree Search. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Automated Reasoning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1007/s10817-020-09576-7
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      – Code: eng
        Text: English
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        PageCount: 34
        StartPage: 287
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      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Logic programming
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Matrices (Mathematics)
        Type: general
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      – TitleFull: Machine Learning Guidance for Connection Tableaux.
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            NameFull: Färber, Michael
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            NameFull: Kaliszyk, Cezary
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            NameFull: Urban, Josef
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              Text: Feb2021
              Type: published
              Y: 2021
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              Value: 65
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            – TitleFull: Journal of Automated Reasoning
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