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MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance

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Book cover Automated Reasoning (IJCAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5195))

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Abstract

This paper describes a system combining model-based and learning-based methods for automated reasoning in large theories, i.e. on a large number of problems that use many axioms, lemmas, theorems, definitions, and symbols, in a consistent fashion. The implementation is based on the existing MaLARea system, which cycles between theorem proving attempts and learning axiom relevance from successes. This system is extended by taking into account semantic relevance of axioms, in a way similar to that of the SRASS system. The resulting combined system significantly outperforms both MaLARea and SRASS on the MPTP Challenge large theory benchmark, in terms of both the number of problems solved and the time taken to find solutions. The design, implementation, and experimental testing of the system are described here.

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Author information

Authors and Affiliations

  1. Charles University, Czech Republic

    Josef Urban, Petr Pudlák & Jiří Vyskočil

  2. University of Miami, USA

    Geoff Sutcliffe

Authors
  1. Josef Urban
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  2. Geoff Sutcliffe
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  3. Petr Pudlák
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  4. Jiří Vyskočil
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Alessandro Armando Peter Baumgartner Gilles Dowek

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© 2008 Springer-Verlag Berlin Heidelberg

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Urban, J., Sutcliffe, G., Pudlák, P., Vyskočil, J. (2008). MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance. In: Armando, A., Baumgartner, P., Dowek, G. (eds) Automated Reasoning. IJCAR 2008. Lecture Notes in Computer Science(), vol 5195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71070-7_37

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  • DOI: https://doi.org/10.1007/978-3-540-71070-7_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71069-1

  • Online ISBN: 978-3-540-71070-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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