Coevolving Defender Strategies Within Adversarial Ground Station Transit Time Games via Competitive Coevolution

Emerging Proliferated Low Earth Orbit (P-LEO) constellations may be susceptible to attacks launched by malevolent actors capable of compromising orbiting satellites. We introduce and investigate an adversarial ground station transit time game as a proxy to study the ability to rapidly detect and respond to satellite attacks. Investigation of this problem allows us to study more complex real-world dynamics involving threats to satellite systems. Unfortunately, the problem proves to be daunting to solve due to the high-dimensionality of the solution space and the difficulty of predicting action consequences in dynamic adversarial settings. For this reason, an effective method to identify successful strategies as solutions to the problem is necessary. In this work, an artificial intelligence approach called competitive coevolution is employed to solve scenarios featuring an attacker evolving strategic locations to degrade the performance of a constellation, while a defender evolves intelligent strategies to counter the attacker’s action. The proposed solution outperforms both minimal and complex strategies, while showing versatility and robustness over a variety of scenarios.