Fuzzy logic-predicate network

DOI

10.2991/eusflat-19.2019.2How to use a DOI?

Keywords

hierarchical description logic-predicate recognition network fuzzy recognition

Abstract

In many Artificial Intelligence problems an investigated object is considered as a set of its elements {ω₁,...,ω_t} and is characterized by properties of these elements and relations between them. These properties and relations may be set by predicates p₁,..., p_n. 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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tatiana Kosovskaia
PY  - 2019/08
DA  - 2019/08
TI  - Fuzzy logic-predicate network
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 9
EP  - 13
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.2
DO  - 10.2991/eusflat-19.2019.2
ID  - Kosovskaia2019/08
ER  -