MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance

  • Josef Urban
  • Geoff Sutcliffe
  • Petr Pudlák
  • Jiří Vyskočil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5195)


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.


Automate Reasoning Large Theory Semantic Relevance Proof Attempt Shared Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Josef Urban
    • 1
  • Geoff Sutcliffe
    • 2
  • Petr Pudlák
    • 1
  • Jiří Vyskočil
    • 1
  1. 1.Charles UniversityCzech Republic
  2. 2.University of MiamiUSA

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