Integrating machine learning to automate the assessment of air traffic control skills in simulators

Abstract

The paper discusses an approach to automating the assessment of air traffic controllers' skills in simulators using machine learning. It describes algorithms that enable objective analysis of controllers' actions, improving the accuracy and speed of evaluation. The integration of these technologies is aimed at enhancing controller training by personalizing learning processes and increasing the overall efficiency of training systems.