A method for planning an agent behavior in the environment of an intellectual training system for the training of air traffic controllers

DOI: 10.31673/2412-4338.2020.018898

Abstract

The article proves that the intellectualization of education is an important area of increasing its effectiveness. To train air traffic controllers, a training system is used, an important element of which is the environment that provides the creation of a simulation environment. Obtaining the environment of an intelligent educational system for training air traffic controllers close to real-world objects is possible using an agent-based approach. The central element in the formation of an agent-oriented environment is agent behavior planning. The article presents an improved method for planning the behavior of agents in the environment of a training system for training air traffic control dispatchers, based on a modified method for generating a set of fuzzy binary execution conditions and continuing elementary plans using a set of fuzzy rules. The static implementation of binary relations in the basic method is its drawback. The development of the method of synthesis of binary relations consists in setting up a lot of parameters, as well as using fuzzy logic, characterizing the desirability of agents starting to execute elementary plans. The developed approach to improving the methods of planning the behavior of an intelligent agent allows you to use once received data at different stages of the operation of the algorithms. This solution can be considered as a preliminary optimization of the subsystem using these methods.
Based on the obtained values, multiagent medium settings and experiments were carried out, the statistical analysis of the results of which made it possible to evaluate the practical effectiveness of the developed methods. The results of evaluating the effectiveness of training suggest that the use of the developed methods allowed to increase the efficiency of decisions made by the air traffic control controller by 7-19 %.

Keywords: air traffic control dispatcher, environment of an intelligent learning system, multi-agent systems, training, theory of fuzzy sets, learning efficiency.

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