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Extending E Prover with Similarity Based Clause Selection Strategies

  • Jan Jakubův
  • Josef Urban
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9791)

Abstract

E prover is a state-of-the-art theorem prover for first-order logic with equality. E prover is built around a saturation loop, where new clauses are derived by inference rules from previously derived clauses. Selection of clauses for the inference provides the main source of non-determinism and an important choice-point of the loop where the right choice can dramatically influence the proof search. In this work we extend E Prover with several new clause selection strategies based on similarity of a clause with the conjecture. In particular, clauses which are more related to the conjecture are preferred. We implement different strategies that define the relationship with a conjecture in different ways. We provide an implementation of the proposed selection strategies and we evaluate their efficiency on an extensive benchmark set.

Keywords

Automated theorem proving Large theory reasoning Clause selection 

References

  1. 1.
    Alama, J., et al.: Premise selection for mathematics by corpus analysis and kernel methods. J. Autom. Reason. 52(2), 191–213 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Kaliszyk, C., Urban, J., Vyskocil, J.: Efficient semantic features for automated reasoning over large theories. In: IJCAI, vol. 15 (2015)Google Scholar
  3. 3.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 2nd edn. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  4. 4.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)MathSciNetzbMATHGoogle Scholar
  5. 5.
    McCune, W.W.: Otter 3.0 Reference Manual and Guide, vol. 9700. Argonne National Laboratory, Argonne (1994)CrossRefGoogle Scholar
  6. 6.
    Schulz, S.: E - a brainiac theorem prover. AI Commun. 15(2), 111–126 (2002)zbMATHGoogle Scholar
  7. 7.
    Urban, J.: BliStr: the blind strategymaker. In: Global Conference on Artificial Intelligence, GCAI 2015, vol. 36, pp. 312–319. EasyChair (2015)Google Scholar
  8. 8.
    Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM J. Comput. 18(6), 1245–1262 (1989)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CIIRCCzech Technical UniversityPragueCzech Republic

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