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Proof Mining with Dependent Types

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Intelligent Computer Mathematics (CICM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10383))

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Abstract

Several approaches exist to data-mining big corpora of formal proofs. Some of these approaches are based on statistical machine learning, and some – on theory exploration. However, most are developed for either untyped or simply-typed theorem provers. In this paper, we present a method that combines statistical data mining and theory exploration in order to analyse and automate proofs in dependently typed language of Coq.

The work was supported by EPSRC grants EP/J014222/1 and EP/K031864/1.

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Author information

Authors and Affiliations

  1. Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK

    Ekaterina Komendantskaya

  2. Mathematics and Computer Science, University of La Rioja, Rioja, Spain

    Jónathan Heras

Authors
  1. Ekaterina Komendantskaya
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  2. Jónathan Heras
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Corresponding author

Correspondence to Ekaterina Komendantskaya .

Editor information

Editors and Affiliations

  1. Radboud University, Nijmegen, The Netherlands

    Herman Geuvers

  2. Coventry University, Coventry, United Kingdom

    Matthew England

  3. National University of Sciences and Technology, Islamabad, Pakistan

    Osman Hasan

  4. Jacobs University Bremen, Bremen, Germany

    Florian Rabe

  5. FIZ Karlsruhe, Berlin, Germany

    Olaf Teschke

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Komendantskaya, E., Heras, J. (2017). Proof Mining with Dependent Types. In: Geuvers, H., England, M., Hasan, O., Rabe, F., Teschke, O. (eds) Intelligent Computer Mathematics. CICM 2017. Lecture Notes in Computer Science(), vol 10383. Springer, Cham. https://doi.org/10.1007/978-3-319-62075-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-62075-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62074-9

  • Online ISBN: 978-3-319-62075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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