Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers

Abstract

This chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype of the neural network for evaluating the timeliness and correctness of the decision making by ATCs has been developed. The new theoretical and practical tasks for simulation and pre-simulation training have been obtained using expert judgment method. The methodology for sentiment analyzing the airline customers' opinions has been proposed. In addition, the examples of artificial intelligence systems and expert systems by the authors, students and colleagues from National Aviation University, Ukraine and Gdansk University of Technology, Poland have been proposed.

Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

“…They argue that the current stateof-the-art ML models show great ability in processing high-dimensional time-series data (like the sequential, time-stamped data generated by a flight simulator). Shmelova et al [13] have proposed to use ML and text analysis to train the air traffic controllers to decrease human factor-related accident rates. Li et al [14] have explored the factors in flight simulators that could improve the human-machine interaction efficiency.…”