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FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F15%3A00311239" target="_blank" >RIV/68407700:21730/15:00311239 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-662-48899-7_7" target="_blank" >http://dx.doi.org/10.1007/978-3-662-48899-7_7</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-662-48899-7_7" target="_blank" >10.1007/978-3-662-48899-7_7</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover

  • Original language description

    FEMaLeCoP is a connection tableau theorem prover based on leanCoP which uses efficient implementation of internal learning-based guidance for extension steps. Despite the fact that exhaustive use of such internal guidance can incur a significant slowdown of the raw inferencing process, FEMaLeCoP trained on related proofs can prove many problems that cannot be solved by leanCoP. In particular on the MPTP2078 benchmark, FEMaLeCoP adds 90 (15.7%) more problems to the 574 problems that are provable by leanCoP. FEMaLeCoP is thus the first AI/ATP system convincingly demonstrating that guiding the internal inference algorithms of theorem provers by knowledge learned from previous proofs can significantly improve the performance of the provers. This paper describes the system, discusses the technology developed, and evaluates the system.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2015

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Logic for Programming, Artificial Intelligence, and Reasoning - 20th International Conference, LPAR-20 2015, Suva, Fiji, November 24-28, 2015, Proceedings

  • ISBN

    978-3-662-48898-0

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    9

  • Pages from-to

    88-96

  • Publisher name

    Bertelsmann Springer CZ

  • Place of publication

    Praha

  • Event location

    Suva

  • Event date

    Nov 24, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000375574900007