Defending against large adversarial drone swarms requires coordination methods that scale effectively beyond conventional multi-agent optimisation. In this paper, we propose to scale strategies proven effective in small defender teams by integrating them as modular components of larger forces using our proposed framework. A dynamic programming (DP) decomposition assembles these components into large teams in polynomial time, enabling efficient construction of scalable defenses without exhaustive evaluation. Because a unit that is strong in isolation may not remain strong when combined, we sample across multiple small-team candidates. Our framework iterates between evaluating large-team outcomes and refining the pool of modular components, allowing convergence on increasingly effective strategies. Experiments demonstrate that this partitioning approach scales to substantially larger scenarios while preserving effectiveness and revealing cooperative behaviours that direct optimisation cannot reliably discover.

翻译：防御大规模敌对无人机集群需要超越传统多智能体优化的协调方法。本文提出通过将已验证有效的小型防御团队策略作为模块化组件，整合到更庞大的防御力量中，从而实现策略的规模化扩展。我们提出的框架采用动态规划分解方法，在多项式时间内将这些模块化组件组装成大型团队，从而无需穷举评估即可高效构建可扩展的防御体系。由于孤立状态下表现优异的单元在组合后未必能保持优势，我们对多个小型团队候选策略进行采样。该框架通过迭代评估大型团队作战效果与优化模块化组件池，使策略逐步收敛至更高效的状态。实验表明，这种分区方法能够扩展到显著更大规模的作战场景，在保持防御效能的同时，还能揭示直接优化方法难以可靠发现的协同行为模式。