SIGMAS：基于二次交互的多智能体群集分组
SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms
AI总结：
SIGMAS通过二次交互建模实现多智能体群集的自监督分组，准确恢复潜在群体结构并平衡个体与集体动态。
摘要：
群集系统，如无人机编队和机器人团队，其复杂动态由个体行为和群体层面的涌现交互共同驱动。与传统的多智能体领域如行人队伍或交通系统不同，群集通常由几个大群体组成，具有固有且持续的成员关系，因此群体识别对于理解细粒度行为至关重要。我们引入了在重叠多智能体群集中的群体预测新任务，其中潜在的群体结构必须直接从智能体轨迹中推断出来，而无需真实的监督。为了解决这一挑战，我们提出了SIGMAS（基于多智能体群集的二次交互分组），一种自监督框架，它超越了直接的成对交互，并在智能体之间建模二次交互。通过捕捉智能体如何与他人相互作用的相似性，SIGMAS能够实现稳健的群体推断，并通过可学习的门控机制在联合推理中自适应地平衡个体和集体动态。在多样化的合成群集场景中的实验表明，SIGMAS能够准确恢复潜在的群体结构，并在同时重叠的群集动态下保持稳健，从而建立了一个新的基准任务和一个群集理解的原理性建模框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
