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System Description: E.T. 0.1

  • Cezary Kaliszyk
  • Stephan Schulz
  • Josef Urban
  • Jiří Vyskočil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9195)

Abstract

E.T. 0.1 is a meta-system specialized for theorem proving over large first-order theories containing thousands of axioms. Its design is motivated by the recent theorem proving experiments over the Mizar, Flyspeck and Isabelle data-sets. Unlike other approaches, E.T. does not learn from related proofs, but assumes a situation where previous proofs are not available or hard to get. Instead, E.T. uses several layers of complementary methods and tools with different speed and precision that ultimately select small sets of the most promising axioms for a given conjecture. Such filtered problems are then passed to E, running a large number of suitable automatically invented theorem-proving strategies. On the large-theory Mizar problems, E.T. considerably outperforms E, Vampire, and any other prover that does not learn from related proofs. As a general ATP, E.T. improved over the performance of unmodified E in the combined FOF division of CASC 2014 by 6 %.

Keywords

Large Problem Related Clause Recursive Call Automate Theorem Prove Training Problem 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Cezary Kaliszyk
    • 1
  • Stephan Schulz
    • 2
  • Josef Urban
    • 3
  • Jiří Vyskočil
    • 4
  1. 1.University of InnsbruckInnsbruckAustria
  2. 2.DHBW StuttgartStuttgartGermany
  3. 3.Radboud University NijmegenNijmegenThe Netherlands
  4. 4.Czech Technical University in PraguePragueCzech Republic

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