MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance

  • Authors:

  • Josef Urban;Geoff Sutcliffe;Petr Pudlák;Jiří Vyskočil

  • Affiliations:

  • Charles University, Czech Republic;University of Miami, USA;Charles University, Czech Republic;Charles University, Czech Republic

  • Venue:
  • IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
  • Year:
  • 2008

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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 MaLAReasystem, 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 SRASSsystem. The resulting combined system significantly outperforms both MaLAReaand SRASSon 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.