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DOI: 10.4230/LIPIcs.ITP.2022.16

The Isabelle ENIGMA

Authors: Zarathustra A. Goertzel, Jan Jakubův, Cezary Kaliszyk, Miroslav Olšák, Jelle Piepenbrock, and Josef Urban

Published in: LIPIcs, Volume 237, 13th International Conference on Interactive Theorem Proving (ITP 2022)

Abstract

We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways. In particular, we develop targeted versions of the ENIGMA guidance for the Isabelle problems, targeted versions of neural premise selection, and targeted strategies for E. The methods are trained in several iterations over hundreds of thousands untyped and typed first-order problems extracted from Isabelle. Our final best single-strategy ENIGMA and premise selection system improves the best previous version of E by 25.3% in 15 seconds, outperforming also all other previous ATP and SMT systems.

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Zarathustra A. Goertzel, Jan Jakubův, Cezary Kaliszyk, Miroslav Olšák, Jelle Piepenbrock, and Josef Urban. The Isabelle ENIGMA. In 13th International Conference on Interactive Theorem Proving (ITP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 237, pp. 16:1-16:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{goertzel_et_al:LIPIcs.ITP.2022.16,
  author =	{Goertzel, Zarathustra A. and Jakub\r{u}v, Jan and Kaliszyk, Cezary and Ol\v{s}\'{a}k, Miroslav and Piepenbrock, Jelle and Urban, Josef},
  title =	{{The Isabelle ENIGMA}},
  booktitle =	{13th International Conference on Interactive Theorem Proving (ITP 2022)},
  pages =	{16:1--16:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-252-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{237},
  editor =	{Andronick, June and de Moura, Leonardo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITP.2022.16},
  URN =		{urn:nbn:de:0030-drops-167253},
  doi =		{10.4230/LIPIcs.ITP.2022.16},
  annote =	{Keywords: E Prover, ENIGMA, Premise Selection, Isabelle/Sledgehammer}
}

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DOI: 10.4230/LIPIcs.SAT.2022.7

Towards Learning Quantifier Instantiation in SMT

Authors: Mikoláš Janota, Jelle Piepenbrock, and Bartosz Piotrowski

Published in: LIPIcs, Volume 236, 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)

Abstract

This paper applies machine learning (ML) to solve quantified satisfiability modulo theories (SMT) problems more efficiently. The motivating idea is that the solver should learn from already solved formulas to solve new ones. This is especially relevant in classes of similar formulas. We focus on the enumerative instantiation - a well-established approach to solving quantified formulas anchored in the Herbrand’s theorem. The task is to select the right ground terms to be instantiated. In ML parlance, this means learning to rank ground terms. We devise a series of features of the considered terms and train on them using boosted decision trees. In particular, we integrate the LightGBM library into the SMT solver cvc5. The experimental results demonstrate that the ML-guided solver enables us to solve more formulas than the base solver and reduce the number of quantifier instantiations. We also do an ablation study on the features used in the machine learning component, showing the contributions of the various additions.

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Mikoláš Janota, Jelle Piepenbrock, and Bartosz Piotrowski. Towards Learning Quantifier Instantiation in SMT. In 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 236, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{janota_et_al:LIPIcs.SAT.2022.7,
  author =	{Janota, Mikol\'{a}\v{s} and Piepenbrock, Jelle and Piotrowski, Bartosz},
  title =	{{Towards Learning Quantifier Instantiation in SMT}},
  booktitle =	{25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)},
  pages =	{7:1--7:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-242-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{236},
  editor =	{Meel, Kuldeep S. and Strichman, Ofer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2022.7},
  URN =		{urn:nbn:de:0030-drops-166810},
  doi =		{10.4230/LIPIcs.SAT.2022.7},
  annote =	{Keywords: satisfiability modulo theories, quantifier instantiation, machine learning}
}

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The Isabelle ENIGMA

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Towards Learning Quantifier Instantiation in SMT

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