An automatic pilot assessment capability using machine learning algorithms that can inform a flight instructor during a flight training session in full flight simulators is proposed in this paper. The current research explores a hybrid expert system and machine learning capability to assess pilot performance in flight simulation. Hybrid rule-based and machine learning algorithms are considered in the approach. Assessing a pilot’s performance during a flight training session is a capability that can considerably improve the effectiveness of a training session and help the flight instructor provide better instructions and feedback.  In this paper, we investigate an efficient way to build an automatic objective assessment engine, that provides a performance index that uses both knowledge of subject matter experts and instructors to train the artificial intelligence capability. By using multi-labels that have the same meaning but come from different sources of knowledge, we demonstrate that an automatic assessment engine is able to reduce the subjectivity of the instructor and optimize the time of the rules creation, tuning, and testing effort for the expert system development. In addition, we show that this hybrid approach increases the accuracy and precision of the assessment of pilot maneuvers during training sessions by using a consensus methodology that blends the multiple sources of knowledge.

Keywords
FLIGHT TRAINING

Additional Keywords
Machine Learning, Performance Assessment, Instructor Standardisation