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Enhancing ENIGMA Given Clause Guidance

  • Jan JakubůvEmail author
  • Josef Urban
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11006)

Abstract

ENIGMA is an efficient implementation of learning-based guidance for given clause selection in saturation-based automated theorem provers. In this work, we describe several additions to this method. This includes better clause features, adding conjecture features as the proof state characterization, better data pre-processing, and repeated model learning. The enhanced ENIGMA is evaluated on the MPTP2078 dataset, showing significant improvements.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Czech Technical University in PraguePragueCzech Republic

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