Modeling pilot flight performance on take-off task with QN-ACTR

Cognitive architecture models can help the simulation and prediction of human performance in complicated human-machine systems. In the current work, we demonstrate a pilot model that can complete takeoff tasks. The model was constructed in Queueing Network-Adaptive Control of Thought Rational (QN-ACTR) cognitive architecture and can be connected to X-Plane to generate various statistics, including performance, mental effort, and situational awareness. The model outcomes are determined in combination with declarative knowledge, chunks, production rules, and a set of parameters. Currently, the model can simulate fly operation behavior similar to human pilots in various conditions. In the future, with additional refinement, we anticipate this model can assist interface evaluation and competency-based pilot training, giving a theory-based prediction method supplementary to human-in-the-loop investigations for research and development in the aviation industry.