Modeling Pilot Flight Performance on Pre-flight and Take-off Tasks with A Cognitive Architecture
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Models of cognitive architecture can be used to simulate and forecast human performance in complicated human-machine systems. The current work demonstrates a pilot model capable of performing and simulating pre-flight preparation and take-off duties. The model was developed using the Queueing Network-Adaptive Control of Thought-Rational (QN-ACTR) cognitive architecture and can be connected to flight simulators like X-Plane to create various data types such as performance and mental workload. Declarative knowledge chunks, production rules, and a collection of parameters all contribute to the model’s output. A human experiment involving pre-flight and take-off tasks was conducted to acquire the data required for the model’s development. At the moment, the model can generate flight operation behaviors that are comparable to that of human pilots. By comparing model data to human data, it was demonstrated that QN-ACTR may be utilized to develop a multi-task model that accurately simulates pilot behavior. With further refinement, including support for situational awareness simulations, such models may help to evaluate interfaces and competency-based pilot training, complementing human-in-the-loop (HITL) experiments in aviation research and development.
Cite this version of the work
Rongbing Xu (2022). Modeling Pilot Flight Performance on Pre-flight and Take-off Tasks with A Cognitive Architecture. UWSpace. http://hdl.handle.net/10012/18174