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FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover

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Book cover Logic for Programming, Artificial Intelligence, and Reasoning (LPAR 2015)

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Abstract

FEMaLeCoP is a connection tableau theorem prover based on leanCoP which uses efficient implementation of internal learning-based guidance for extension steps. Despite the fact that exhaustive use of such internal guidance can incur a significant slowdown of the raw inferencing process, FEMaLeCoP trained on related proofs can prove many problems that cannot be solved by leanCoP. In particular on the MPTP2078 benchmark, FEMaLeCoP adds 90 (15.7 %) more problems to the 574 problems that are provable by leanCoP. FEMaLeCoP is thus the first AI/ATP system convincingly demonstrating that guiding the internal inference algorithms of theorem provers by knowledge learned from previous proofs can significantly improve the performance of the provers. This paper describes the system, discusses the technology developed, and evaluates the system.

C. Kaliszyk—Supported by the Austrian Science Fund (FWF): P26201.

J. Urban—Supported by NWO grant nr. 612.001.208 and ERC Consolidator grant nr. 649043 AI4REASON.

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Notes

  1. 1.

    The hardware used is Intel Xeon E7-4870 2.30 GHz with 256 GB RAM.

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Author information

Authors and Affiliations

  1. University of Innsbruck, Innsbruck, Austria

    Cezary Kaliszyk

  2. Czech Technical University in Prague, Prague, Czech Republic

    Josef Urban

Authors
  1. Cezary Kaliszyk
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  2. Josef Urban
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Correspondence to Cezary Kaliszyk .

Editor information

Editors and Affiliations

  1. New York University, New York, New York, USA

    Martin Davis

  2. University of the South Pacific, Suva, Fiji

    Ansgar Fehnker

  3. Macquarie University, Sydney, New South Wales, Australia

    Annabelle McIver

  4. The University of Manchester, Manchester, United Kingdom

    Andrei Voronkov

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Kaliszyk, C., Urban, J. (2015). FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover. In: Davis, M., Fehnker, A., McIver, A., Voronkov, A. (eds) Logic for Programming, Artificial Intelligence, and Reasoning. LPAR 2015. Lecture Notes in Computer Science(), vol 9450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48899-7_7

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  • DOI: https://doi.org/10.1007/978-3-662-48899-7_7

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  • Print ISBN: 978-3-662-48898-0

  • Online ISBN: 978-3-662-48899-7

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