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An Augmented MetiTarski Dataset for Real Quantifier Elimination Using Machine Learning

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Intelligent Computer Mathematics (CICM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14101))

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Abstract

We contribute a new dataset composed of more than 41K MetiTarski challenges that can be used to investigate applications of machine learning (ML) in determining efficient variable orderings in Cylindrical Algebraic Decomposition. The proposed dataset aims to address inadvertent bias issues present in prior benchmarks, paving the way to development of robust, easy-to-generalize ML models.

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR00112290064. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Government or DARPA.

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Notes

  1. 1.

    https://github.com/SRI-CSL/augmented-metitarski.

  2. 2.

    https://scikit-learn.org/stable/.

  3. 3.

    https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html.

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Author information

Authors and Affiliations

  1. Imandra Inc., Austin, TX, 78704, USA

    John Hester & Grant Passmore

  2. SRI International, Menlo Park, CA, 94025, USA

    Briland Hitaj, Sam Owre, Natarajan Shankar & Eric Yeh

Authors
  1. John Hester
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  2. Briland Hitaj
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  3. Grant Passmore
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  4. Sam Owre
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  5. Natarajan Shankar
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  6. Eric Yeh
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Corresponding author

Correspondence to Grant Passmore .

Editor information

Editors and Affiliations

  1. ENSIIE, Evry, France

    Catherine Dubois

  2. University of Birmingham, Birmingham, UK

    Manfred Kerber

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Hester, J., Hitaj, B., Passmore, G., Owre, S., Shankar, N., Yeh, E. (2023). An Augmented MetiTarski Dataset for Real Quantifier Elimination Using Machine Learning. In: Dubois, C., Kerber, M. (eds) Intelligent Computer Mathematics. CICM 2023. Lecture Notes in Computer Science(), vol 14101. Springer, Cham. https://doi.org/10.1007/978-3-031-42753-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-42753-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42752-7

  • Online ISBN: 978-3-031-42753-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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