Agent-Based Evolutionary Predator-Prey Strategies

This paper discusses work integrating emergent behavior models with digital genetic evolution methods into intelligent agents for missile combat simulations. Drawing from predator-prey dynamics, proportional navigation, and decentralized decision-making, missile agents with real-time sensor inputs can dynamically adjust trajectories, optimize resource allocation, and achieve mission objectives in contested environments. While deterministic optimization is suitable for premission planning, evolutionary algorithms and decentralized agent-based systems are more adaptable in dynamic environments. Emergent behaviors lead to emergent intelligence, inspired by biological systems. Footage of social behaviors is used for seeding initial agent personality structures. Genetic evolution algorithms optimize self-organizing in forms inaccessible to traditional top-down methods, enabling agents to adapt to changing conditions, scale effectively, reveal network degradation effects, and maintain alignment with global objectives. Missile dynamics, such as turn radius, speed, range, and aerodynamic stability, are modeled within the predator-prey framework for realism. This model addresses scalability, real-time adaptability, and system-level coordination in missile combat scenarios. Specific architecture and integration into larger decision-making tools is discussed, focusing on the potential of emergent intelligence to uncover complex tactics in dynamic combat environments.