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Towards Meta-interpretive Learning of Programming Language Semantics

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Inductive Logic Programming (ILP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11770))

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Abstract

We introduce a new application for inductive logic programming: learning the semantics of programming languages from example evaluations. In this short paper, we explore a simplified task in this domain using the Metagol meta-interpretive learning system. We highlight the challenging aspects of this scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and propose extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.

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Acknowledgments

The authors wish to thank Andrew Cropper, Vaishak Belle, and anonymous reviewers for comments. This work was supported by ERC Consolidator Grant Skye (grant number 682315).

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Authors and Affiliations

  1. Laboratory for Foundations of Computer Science, University of Edinburgh, Edinburgh, Scotland

    Sándor Bartha & James Cheney

  2. The Alan Turing Institute, London, UK

    James Cheney

Authors
  1. Sándor Bartha
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  2. James Cheney
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Corresponding author

Correspondence to Sándor Bartha .

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Editors and Affiliations

  1. Department of Computer Science, University of York, Heslington, UK

    Dimitar Kazakov

  2. Department of Computer Science, University of York, Heslington, UK

    Can Erten

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Bartha, S., Cheney, J. (2020). Towards Meta-interpretive Learning of Programming Language Semantics. In: Kazakov, D., Erten, C. (eds) Inductive Logic Programming. ILP 2019. Lecture Notes in Computer Science(), vol 11770. Springer, Cham. https://doi.org/10.1007/978-3-030-49210-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-49210-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49209-0

  • Online ISBN: 978-3-030-49210-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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