通过分析代理间交互模式，自动识别重叠群体结构。
SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms
- 基于二阶交互建模，捕捉代理如何相似地与其他代理互动
- 在多种合成场景中准确恢复隐藏群体结构，支持重叠动态
- 适合研究无人机编队、机器人团队等复杂群体行为
蜂群系统（如无人机编队和机器人团队）表现出由个体行为与涌现群体交互驱动的复杂动态。与行人流或交通系统等传统多智能体系统不同，蜂群通常由少数具有固有且持久成员关系的大群体组成，因此群体识别对理解细粒度行为至关重要。我们提出重叠多智能体蜂群中的群体预测新任务，即从代理轨迹中无监督地推断潜在群体结构。为此，我们提出SIGMAS（基于二阶交互的多智能体蜂群分组框架），该框架超越直接成对交互，建模代理间的二阶交互。通过捕捉代理如何相似地与其他代理互动，SIGMAS实现稳健的群体推断，并通过可学习门控机制自适应平衡个体与集体动态，以实现联合推理。在多种合成蜂群场景中的实验表明，SIGMAS能准确恢复潜在群体结构，并在同时重叠的蜂群动态下保持鲁棒性，既建立了新基准任务，也提供了蜂群理解的原理性建模范式。
原文摘要 · Abstract (English)
Swarming systems, such as drone fleets and robotic teams, exhibit complex dynamics driven by both individual behaviors and emergent group-level interactions. Unlike traditional multi-agent domains such as pedestrian crowds or traffic systems, swarms typically consist of a few large groups with inherent and persistent memberships, making group identification essential for understanding fine-grained behavior. We introduce the novel task of group prediction in overlapping multi-agent swarms, where latent group structures must be inferred directly from agent trajectories without ground-truth supervision. To address this challenge, we propose SIGMAS (Second-order Interaction-based Grouping for Multi-Agent Swarms), a self-supervised framework that goes beyond direct pairwise interactions and model second-order interaction across agents. By capturing how similarly agents interact with others, SIGMAS enables robust group inference and adaptively balances individual and collective dynamics through a learnable gating mechanism for joint reasoning. Experiments across diverse synthetic swarm scenarios demonstrate that SIGMAS accurately recovers latent group structures and remains robust under simultaneously overlapping swarm dynamics, establishing both a new benchmark task and a principled modeling framework for swarm understanding.
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