Technology Category
Machine Learning: Unsupervised & Self-Supervised LearningMultiagent Systems: Agent-Based Simulation and Emergent BehaviorHumans and AI: Human-Aware Planning and Behavior Prediction
SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms
🤖 AI Summary
This work addresses the problem of inferring overlapping multi-agent group structures from trajectory data in unsupervised settings by proposing a novel self-supervised framework. It formalizes, for the first time, the task of overlapping group prediction through modeling second-order interactions—defined as similarities in interaction patterns among agents—and introduces a learnable gating mechanism to jointly optimize representations of both individual and group dynamics. In contrast to conventional approaches that rely solely on first-order interactions, the proposed framework significantly improves the accuracy and robustness of recovering latent group structures in complex, dynamically overlapping scenarios. This advancement establishes a new benchmark and modeling paradigm for understanding multi-agent group organization.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
group prediction
overlapping multi-agent swarms
latent group structures
trajectory-based inference
unsupervised grouping
Innovation
Methods, ideas, or system contributions that make the work stand out.
second-order interaction
self-supervised grouping
overlapping multi-agent swarms
latent group inference
learnable gating mechanism
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