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A First-Order Axiomatization for Transition Learning with RichConstraints

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00332228" target="_blank" >RIV/68407700:21230/17:00332228 - isvavai.cz</a>

  • Result on the web

    <a href="http://ceur-ws.org/Vol-2085/barvinekLBP-ILP2017.pdf" target="_blank" >http://ceur-ws.org/Vol-2085/barvinekLBP-ILP2017.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A First-Order Axiomatization for Transition Learning with RichConstraints

  • Original language description

    We address the task of learning a dynamic Boolean network model from dataabout its state transitions, and constraints regarding the known attractors ofthe system. To this end, we propose a learning strategy where such priorknowledge is compiled into a first-order theory along with axioms describ-ing the Boolean transitions functions and the language bias for their repre-sentation. The learning task is then posed as a model-finding task, in that theBoolean network is obtained as a model of the input first-order theory. Withthis framework, we support experimentally the hypothesis that attractor con-straints reduce the number of state transition examples needed to identify thetarget model.

  • 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

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Late Breaking Papers of the 27th International Conference on Inductive Logic Programming

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    1-5

  • Publisher name

    CEUR Workshop Proceedings

  • Place of publication

    Tilburg

  • Event location

    Orléans

  • Event date

    Sep 4, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article