Fuzzy logic-predicate network

Research output

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
EditorsMartin Stepnicka
Pages9-13
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
Volume1
ISSN (Print)2589-6644

Conference

ConferenceThe 11th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT-2019
Abbreviated titleEUSFLAT-2019
CountryCzech Republic
CityПрага
Period9/09/1913/09/19

Fingerprint

Predicate
Fuzzy Logic
Fuzzy logic
Artificial intelligence
Computational complexity
NP-complete problem
Predicate Logic
Object
Artificial Intelligence
Computational Complexity
Calculate
Decrease
Configuration
Vertex of a graph
Class

Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

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).
Kosovskaia, Tatiana. / Fuzzy logic-predicate network. Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). editor / Martin Stepnicka. 2019. pp. 9-13 (Atlanties Studies in Uncertainty Modeling).
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title = "Fuzzy logic-predicate network",
abstract = "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.",
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author = "Tatiana Kosovskaia",
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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). Atlanties Studies in Uncertainty Modeling, vol. 1, pp. 9-13, Прага, 9/09/19.

Fuzzy logic-predicate network. / Kosovskaia, Tatiana.

Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). ed. / Martin Stepnicka. 2019. p. 9-13 (Atlanties Studies in Uncertainty Modeling; Vol. 1).

Research output

TY - GEN

T1 - Fuzzy logic-predicate network

AU - Kosovskaia, Tatiana

PY - 2019/9

Y1 - 2019/9

N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 9789462527706

T3 - Atlanties Studies in Uncertainty Modeling

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EP - 13

BT - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)

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Kosovskaia T. Fuzzy logic-predicate network. In Stepnicka M, editor, Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). 2019. p. 9-13. (Atlanties Studies in Uncertainty Modeling).