Two-Sided, Genetics-Based Learning to Discover Novel Fighter Combat Maneuvers
This paper reports the authors’ ongoing experience with a system for discovering novel fighter combat maneuvers, using a genetics-based machine learning process, and combat simulation. In effect, the genetic learning system in this application is taking the place of a test pilot, in discovering complex maneuvers from experience. The goal of this work is distinct from that of many other studies, in that innovation, and discovery of novelty (as opposed to optimality), is in self valuable. This makes the details of aims and techniques somewhat distinct from other genetics-based machine learning research.
This paper presents previously unpublished results that show two co-adapting players in similar aircraft. The complexities of analyzing these results, given the red queen effect are discussed. Finally, general implications of this work are discussed.
KeywordsTest Pilot Machine Learning System Complex Maneuver Machine Learning Process Real Aircraft
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