Multiple Defender

Multiple defender models address the strategic interaction between multiple security agents and adversaries in various domains, aiming to optimize resource allocation and minimize threats. Current research focuses on developing efficient algorithms for coordinating heterogeneous defenders, often employing reinforcement learning, game theory (like Stackelberg Security Games), and novel network architectures (e.g., spiking neural networks) to handle complex scenarios with imperfect information and dynamic environments. These advancements have implications for improving security in diverse applications, from cybersecurity and physical protection to autonomous systems and even sports analytics, by providing more robust and scalable defense strategies.