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Hammering Mizar by Learning Clause Guidance (Short Paper)

Authors Jan Jakubův, Josef Urban

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Subject Classification

ACM Subject Classification
  • Theory of computation → Automated reasoning
  • Computing methodologies → Theorem proving algorithms
  • Computing methodologies → Machine learning
Keywords
  • Proof automation
  • ITP hammers
  • Automated theorem proving
  • Machine learning

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Abstract

We describe a very large improvement of existing hammer-style proof automation over large ITP libraries by combining learning and theorem proving. In particular, we have integrated state-of-the-art machine learners into the E automated theorem prover, and developed methods that allow learning and efficient internal guidance of E over the whole Mizar library. The resulting trained system improves the real-time performance of E on the Mizar library by 70% in a single-strategy setting.

Cite As Get BibTex

Jan Jakubův and Josef Urban. Hammering Mizar by Learning Clause Guidance (Short Paper). In 10th International Conference on Interactive Theorem Proving (ITP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 141, pp. 34:1-34:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.ITP.2019.34

Author Details

Jan Jakubův
  • Czech Technical University in Prague, Czech Republic
Josef Urban
  • Czech Technical University in Prague, Czech Republic

Funding

Supported by the AI4REASON ERC Consolidator grant number 649043, and by the Czech project AI&Reasoning CZ.02.1.01/0.0/0.0/15\_003/0000466 and the European Regional Development Fund.

References

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