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articles

Authors: Frolov I., Borisov V. V.     Published in № 6(108) 25 december 2023 year
Rubric: Models and methods

Scenario-information analysis and modeling of adaptive training of specialists groups based on a fuzzy ontological approach

The article shows the problem of reducing the efficiency and quality of the formation of control actions by the head of the class when preparing groups of specialists to perform complex tasks agreed on time, place and goals in organizational and technical systems of various purposes due to the influence on the psychophysiological capabilities of the head of the class of increasing the density of the flow of incoming information at the stages of working out the interaction between groups of specialists in a single virtual space. The use of intelligent methods to solve this problem is substantiated and a method of scenario-information analysis of adaptive training of groups of specialists is proposed, the essence of which is to build a scenario-information model of adaptive training based on a fuzzy ontological approach, taking into account the availability of resources, the current state of specialists (current level of preparedness) and precedent scenarios of adaptive training for subsequent modeling of this process and estimates of its achievability under various preparation scenarios. A fuzzy ontological approach to modeling adaptive training of groups of specialists using granulation of information resources is proposed, which makes it possible to organize the training process more efficiently, including in conditions of limited time and material resources. The results of experimental studies on improving the level of preparedness of group specialists through intelligent management of their adaptive training are shown.

Key words

scenario-information analysis and modeling of adaptive training of groups of specialists, granulation of information resources, formation of control actions, method of scenario-information analysis, model of a group of specialists, composite fuzzy ontological model

The author:

Frolov I.

Degree:

Cand. Sci. (Eng.), Doctoral Student, Russian Federation Armed Forces Army Air Defense Military Academy

Location:

Smolensk, Russia

The author:

Borisov V. V.

Degree:

Professor, department of Computer Engineering, the Branch of National Research University MPEI in Smolensk

Location:

Smolensk, Russia