MaSh: machine learning for sledgehammer

  • Authors:

  • Daniel Kühlwein;Jasmin Christian Blanchette;Cezary Kaliszyk;Josef Urban

  • Affiliations:

  • ICIS, Radboud Universiteit Nijmegen, The Netherlands;Fakultät für Informatik, Technische Universität München, Germany;Institut für Informatik, Universität Innsbruck, Austria;ICIS, Radboud Universiteit Nijmegen, The Netherlands

  • Venue:
  • ITP'13 Proceedings of the 4th international conference on Interactive Theorem Proving
  • Year:
  • 2013

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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.