A neural network trained to select aircraft maneuvers during air combat: a comparison of network and rule based performance

Research to develop a neural network model that selects aircraft maneuvers in the domain of air-combat maneuvering is described. A methodology for converting rule-based systems into a neural network was established. A comparison between the neural network and a rule-based expert system was undertaken. Differences between the architectures were explored, and hypotheses as to causes of differential performance were made. Both models were compared with expert fighter pilots on a transfer task. The neural network agreed with maneuver selections made by expert fighter pilots 2.5 times more often than the rule-based system. These findings were explained in terms of the ability of neural nets to generalize maneuver selections to novel airspace conditions. Implications of these results were also discussed