This article is cited in 11 papers

Knowledge representation and acquision

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

V. B. Melekhin^ab, M. V. Khachumov<sup>c

a</sup> 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

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.

DOI: 10.14357/20718594200306

 English version: , 2021, 48:5, 333–341

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