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E-MaLeS 1.1

  • Daniel Kühlwein
  • Stephan Schulz
  • Josef Urban
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7898)

Abstract

Picking the right search strategy is important for the success of automatic theorem provers. E-MaLeS is a meta-system that uses machine learning and strategy scheduling to optimize the performance of the first-order theorem prover E. E-MaLeS applies a kernel-based learning method to predict the run-time of a strategy on a given problem and dynamically constructs a schedule of multiple promising strategies that are tried in sequence on the problem. This approach has significantly improved the performance of E 1.6, resulting in the second place of E-MaLeS 1.1 in the FOF divisions of CASC-J6 and CASC@Turing.

Keywords

Strategy Schedule Theorem Prover Automate Reasoning Automatic Mode Automatic Theorem Prover 
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 2013

Authors and Affiliations

  • Daniel Kühlwein
    • 1
  • Stephan Schulz
    • 2
  • Josef Urban
    • 1
  1. 1.Radboud University NijmegenNijmegenNetherlands
  2. 2.Technische Universität MünchenMünchenGermany

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