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An Abstraction-Refinement Framework for Reasoning with Large Theories

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Automated Reasoning (IJCAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10900))

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Abstract

In this paper we present an approach to reasoning with large theories which is based on the abstraction-refinement framework. The proposed approach consists of the following approximations: the over-approximation, the under-approximation and their combination. We present several concrete abstractions based on subsumption, signature grouping and argument filtering. We implemented our approach in a theorem prover for first-order logic iProver and evaluated over the TPTP library.

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Notes

  1. 1.

    Preliminary version of this work was presented at the IWIL workshop [13].

  2. 2.

    iProver is available at: http://www.cs.man.ac.uk/~korovink/iprover/.

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Acknowledgements

We would like to thank anonymous reviewers for many helpful suggestions.

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Authors and Affiliations

  1. School of Computer Science, The University of Manchester, Manchester, UK

    Julio Cesar Lopez Hernandez & Konstantin Korovin

Authors
  1. Julio Cesar Lopez Hernandez
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  2. Konstantin Korovin
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Correspondence to Konstantin Korovin .

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Editors and Affiliations

  1. Université de Lorraine, Vandoeuvre-lès-Nancy, France

    Didier Galmiche

  2. Baden-Wuerttemberg Cooperative State University, Stuttgart, Germany

    Stephan Schulz

  3. University of Trento, Trento, Italy

    Roberto Sebastiani

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Lopez Hernandez, J.C., Korovin, K. (2018). An Abstraction-Refinement Framework for Reasoning with Large Theories. In: Galmiche, D., Schulz, S., Sebastiani, R. (eds) Automated Reasoning. IJCAR 2018. Lecture Notes in Computer Science(), vol 10900. Springer, Cham. https://doi.org/10.1007/978-3-319-94205-6_43

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  • DOI: https://doi.org/10.1007/978-3-319-94205-6_43

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