Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2020, Issue 3, Pages 61–72
DOI: https://doi.org/10.14357/20718594200306
(Mi iipr145)
 

This article is cited in 8 scientific papers (total in 8 papers)

Knowledge representation and acquision

Fuzzy semantic networks as an adaptive model of knowledge representation of autonomous intelligent systems

V. B. Melekhinab, M. V. Khachumovc

a Daghestan State Technical University, Makhachkala, Russia
b Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
c Peoples' Friendship University of Russia named after Patrice Lumumba, Moscow, Russia
Full-text PDF (362 kB) Citations (8)
Abstract: The main features of planning the purposeful behavior of autonomous intelligent systems in conditions of a problem environment varying in degree of a priori uncertainty are considered. A model is developed for representing declarative knowledge of autonomous intelligent systems regardless of a specific subject area based on active and passive fuzzy semantic networks. The application of this model allows autonomous intelligent systems, on the one hand, to plan targeted behavior in a priori underdetermined conditions of a problem environment, and on the other hand, to organize the process of self-learning in a priori undescribed functioning conditions. The operations of comparing fuzzy semantic networks with each other are considered, which allow organizing effective decision-making in the process of planning targeted behavior in the face of uncertainty. The operations of decomposition, composition, and generalization of fuzzy semantic networks have been developed, which serve to organize the planning of the behavior of autonomous intelligent systems in the process of solving complex problems, accompanied by a formal description of current situations of a problem environment with a large dimension.
Keywords: autonomous intelligent system, uncertainty conditions, problematic environment, knowledge representation, fuzzy semantic network, behavior planning.
Funding agency Grant number
Russian Foundation for Basic Research 17-29-07003 офи м
18-07-00025 а
English version:
Scientific and Technical Information Processing, 2021, Volume 48, Issue 5, Pages 333–341
DOI: https://doi.org/10.3103/S0147688221050051
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: V. B. Melekhin, M. V. Khachumov, “Fuzzy semantic networks as an adaptive model of knowledge representation of autonomous intelligent systems”, Artificial Intelligence and Decision Making, 2020, no. 3, 61–72; Scientific and Technical Information Processing, 48:5 (2021), 333–341
Citation in format AMSBIB
\Bibitem{MelKha20}
\by V.~B.~Melekhin, M.~V.~Khachumov
\paper Fuzzy semantic networks as an adaptive model of knowledge representation of autonomous intelligent systems
\jour Artificial Intelligence and Decision Making
\yr 2020
\issue 3
\pages 61--72
\mathnet{http://mi.mathnet.ru/iipr145}
\crossref{https://doi.org/10.14357/20718594200306}
\elib{https://elibrary.ru/item.asp?id=44006440}
\transl
\jour Scientific and Technical Information Processing
\yr 2021
\vol 48
\issue 5
\pages 333--341
\crossref{https://doi.org/10.3103/S0147688221050051}
Linking options:
  • https://www.mathnet.ru/eng/iipr145
  • https://www.mathnet.ru/eng/iipr/y2020/i3/p61
  • This publication is cited in the following 8 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Artificial Intelligence and Decision Making
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    Abstract page:39
    Full-text PDF :18
    References:1
     
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