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International Conference on Interactive Theorem Proving

ITP 2013: Interactive Theorem Proving pp 35–50Cite as

MaSh: Machine Learning for Sledgehammer

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7998))

Abstract

Sledgehammer integrates automatic theorem provers in the proof assistant Isabelle/HOL. A key component, the relevance filter, heuristically ranks the thousands of facts available and selects a subset, based on syntactic similarity to the current goal. We introduce MaSh, an alternative that learns from successful proofs. New challenges arose from our “zero-click” vision: MaSh should integrate seamlessly with the users’ workflow, so that they benefit from machine learning without having to install software, set up servers, or guide the learning. The underlying machinery draws on recent research in the context of Mizar and HOL Light, with a number of enhancements. MaSh outperforms the old relevance filter on large formalizations, and a particularly strong filter is obtained by combining the two filters.

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Author information

Authors and Affiliations

  1. ICIS, Radboud Universiteit Nijmegen, The Netherlands

    Daniel Kühlwein & Josef Urban

  2. Fakultät für Informatik, Technische Universität München, Germany

    Jasmin Christian Blanchette

  3. Institut für Informatik, Universität Innsbruck, Austria

    Cezary Kaliszyk

Authors
  1. Daniel Kühlwein
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  2. Jasmin Christian Blanchette
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  3. Cezary Kaliszyk
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  4. Josef Urban
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Editor information

Editors and Affiliations

  1. IRISA, Université Rennes 1, Campus de Beaulieu, 35042, Rennes Cedex, France

    Sandrine Blazy

  2. Université Paris-Sud, LRI, Bat 650, Univ. Paris Sud, 91405, Orsay Cedex, France

    Christine Paulin-Mohring

  3. Inria Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France

    David Pichardie

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Kühlwein, D., Blanchette, J.C., Kaliszyk, C., Urban, J. (2013). MaSh: Machine Learning for Sledgehammer. In: Blazy, S., Paulin-Mohring, C., Pichardie, D. (eds) Interactive Theorem Proving. ITP 2013. Lecture Notes in Computer Science, vol 7998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39634-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-39634-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39633-5

  • Online ISBN: 978-3-642-39634-2

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