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Monotonicity Inference for Higher-Order Formulas

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Automated Reasoning (IJCAR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6173))

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Abstract

Formulas are often monotonic in the sense that if the formula is satisfiable for given domains of discourse, it is also satisfiable for all larger domains. Monotonicity is undecidable in general, but we devised two calculi that infer it in many cases for higher-order logic. The stronger calculus has been implemented in Isabelle’s model finder Nitpick, where it is used to prune the search space, leading to dramatic speed improvements for formulas involving many atomic types.

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Author information

Authors and Affiliations

  1. Institut für Informatik, Technische Universität München, Germany

    Jasmin Christian Blanchette & Alexander Krauss

Authors
  1. Jasmin Christian Blanchette
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  2. Alexander Krauss
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Editors and Affiliations

  1. RWTH Aachen, LuFG Informatik, 2, Ahornstr. 55, 52074, Aachen, Germany

    Jürgen Giesl

  2. Department of Computer Science, Chalmers University of Technology, 41296, Gothenburg, Sweden

    Reiner Hähnle

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Blanchette, J.C., Krauss, A. (2010). Monotonicity Inference for Higher-Order Formulas. In: Giesl, J., Hähnle, R. (eds) Automated Reasoning. IJCAR 2010. Lecture Notes in Computer Science(), vol 6173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14203-1_8

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  • DOI: https://doi.org/10.1007/978-3-642-14203-1_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14202-4

  • Online ISBN: 978-3-642-14203-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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