Journal of Automated Reasoning

, Volume 57, Issue 3, pp 219–244

A Learning-Based Fact Selector for Isabelle/HOL

Article

DOI: 10.1007/s10817-016-9362-8

Cite this article as:
Blanchette, J.C., Greenaway, D., Kaliszyk, C. et al. J Autom Reasoning (2016) 57: 219. doi:10.1007/s10817-016-9362-8

Abstract

Sledgehammer integrates automatic theorem provers in the proof assistant Isabelle/HOL. A key component, the fact selector, heuristically ranks the thousands of facts (lemmas, definitions, or axioms) available and selects a subset, based on syntactic similarity to the current proof goal. We introduce MaSh, an alternative that learns from successful proofs. New challenges arose from our “zero click” vision: MaSh integrates seamlessly with the users’ workflow, so that they benefit from machine learning without having to install software, set up servers, or guide the learning. MaSh outperforms the old fact selector on large formalizations.

Keywords

Relevance filtering Machine learning Proof assistants  Automatic theorem provers 

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jasmin Christian Blanchette
    • 1
    • 2
  • David Greenaway
    • 3
  • Cezary Kaliszyk
    • 4
  • Daniel Kühlwein
    • 5
  • Josef Urban
    • 6
  1. 1.Inria Nancy – Grand-Est & LORIAVillers-lès-NancyFrance
  2. 2.Max-Planck-Institut für InformatikSaarbrückenGermany
  3. 3.NICTAUniversity of New South WalesSydneyAustralia
  4. 4.University of InnsbruckInnsbruckAustria
  5. 5.Radboud UniversityNijmegenThe Netherlands
  6. 6.Czech Technical University in PraguePragueCzech Republic

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