Fuzzy logic-predicate network

Research outputpeer-review


In many Artificial Intelligence problems an investigated object is considered as a set of its elements {ω1,...,ωt} and is characterized by properties of these elements and relations between them. These properties and relations may be set by predicates p1,..., pn. The problems appeared with such an approach become to be NP-complete or NP-hard ones. To decrease the computational complexity of these problems a hierarсhical many-level description of classes was suggested. A logic-predicate recognition network may be constructed according to such a many-level description. Such a network recognizes only objects which have been in the training set, but it may be easily retrained by a new object. After retraining it may change its configuration, i.e. the number of levels and the number of nodes in every level. A modification of such a network is offered in this paper. This modification allows to do a fuzzy recognition of a new object and to calculate the degree of certainty that this object or its part belongs to some class of objects.
Translated title of the contributionНечёткая логико-предикатная сеть
Original languageEnglish
Title of host publicationProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
EditorsMartin Stepnicka
Publication statusPublished - Sep 2019
Event11th Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology - Чешский институт информатики, робототехники и кибернетики, Прага
Duration: 9 Sep 201913 Sep 2019
Conference number: 11

Publication series

NameAtlanties Studies in Uncertainty Modeling
ISSN (Print)2589-6644


ConferenceThe 11th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT-2019
Abbreviated titleEUSFLAT-2019
CountryCzech Republic

Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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    Kosovskaia, T. (2019). Fuzzy logic-predicate network. In M. Stepnicka (Ed.), Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (pp. 9-13). (Atlanties Studies in Uncertainty Modeling; Vol. 1). https://www.atlantis-press.com/proceedings/eusflat-19/articles