Classifier systems in combat: two-sided learning of maneuvers for advanced fighter aircraft

Abstract

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. 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 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. 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 demonstrated the ability of the genetic learning system to discover novel tactics in a dynamic air combat environment. The results gained favorable evaluation from fighter aircraft test pilots. However, these pilots noted that the static strategy employed by the X-31's opponent was a limitation. 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. The experiments successfully demonstrate both aircraft developing objectively interesting strategies. However, the results also point out the complexity of evaluating results from mutually adaptive players, due to the red queen effect. These complexities, and future directions of the project, are discussed in the paper's conclusions.