However, the results also point out the complexity of evaluating results from mutually adaptive players, due to the red queen effect. The experiments successfully demonstrate both aircraft developing objectively interesting strategies. In response to these comments, this paper reports new results with two-sided learning, where both aircraft in a one-versus-one combat scenario use genetics-based machine learning to adapt their strategies. However, these pilots noted that the static strategy employed by the X-31's opponent was a limitation. The results gained favorable evaluation from fighter aircraft test pilots. This demonstrated the ability of the genetic learning system to discover novel tactics in a dynamic air combat environment. Previous efforts with this system showed that the resulting maneuvers allowed the X-31 to successfully exploit its post-stall capabilities against a conventional fighter opponent. This system, which was based on a learning classifier system approach, employed a digital simulation model of one-versus-one air combat, and a genetic algorithm, to develop effective tactics for the X-31 experimental fighter aircraft. In this project, a genetics-based machine learning system was implemented to generate high angle-of-attack air combat tactics for advanced fighter aircraft. This paper reports the continuing results of a project where a genetics-based machine learning system acquires rules for novel fighter combat maneuvers through simulation.
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