Multi‐Agent Game Strategies for Kill Chain Optimization in Networked Aerial Combat Systems

In modern information warfare, single‐agent and multi‐agent systems (MAS) play a critical role in achieving integrated combat capabilities. This study examines agent‐based game strategies in airborne networked systems, focusing specifically on the air‐fleet kill chain—the central framework that unifies fighters, early‐warning aircraft, and missiles into a cohesive, intelligent system. We analyse how MAS mitigate the complexity, uncertainty, and adversarial dynamics of aerial combat by optimising detection, decision‐making, and strike efficiency through networked collaboration. Two representative scenarios are presented: (1) an AI‐driven fighter (ALPHA AI) using genetic‐fuzzy tree algorithms to surpass human pilots, and (2) adversarial multi‐agent reinforcement learning (MADDPG) in OpenAI's simulation suite. We then propose a systematic MAS‐based kill‐chain optimisation design that integrates deep reinforcement learning, Bayesian inference, and tactical decision frameworks. Simulation results demonstrate enhanced coordination, real‐time adaptability, and optimised damage probabilities in both single‐ and multi‐agent confrontations. Our findings establish a theoretical foundation for transitioning from rule‐based to AI‐driven system‐of‐systems warfare in next‐generation aerial combat.