用事件视觉预测多智能体系统互动强度与收敛时间
Learning Collective Dynamics of Multi-Agent Systems using Event-based Vision
- 直接从视觉数据中学习多智能体集体动态
- 事件流表示比传统帧方法更有效预测行为
- 适合实时群体行为分析与智能系统设计
本文提出一个新问题：基于视觉感知学习并预测多智能体系统的集体动态，重点关注交互强度与收敛时间。多智能体系统定义为超过十个相互作用的智能体集合，表现出复杂群体行为。不同于以往假设已知智能体位置的研究，本文聚焦于深度学习模型，直接从视觉数据（帧或事件）中预测集体动态。由于缺乏相关数据集，我们采用先进的集群模拟器生成仿真数据，并构建视觉到事件的转换框架。实验表明，事件基表示在预测此类集体行为上优于传统帧基方法。基于分析结果，我们提出事件基视觉多智能体动态预测模型（evMAP），该架构旨在实现对交互强度和集体行为涌现的实时、高精度理解。
原文摘要 · Abstract (English)
This paper proposes a novel problem: vision-based perception to learn and predict the collective dynamics of multi-agent systems, specifically focusing on interaction strength and convergence time. Multi-agent systems are defined as collections of more than ten interacting agents that exhibit complex group behaviors. Unlike prior studies that assume knowledge of agent positions, we focus on deep learning models to directly predict collective dynamics from visual data, captured as frames or events. Due to the lack of relevant datasets, we create a simulated dataset using a state-of-the-art flocking simulator, coupled with a vision-to-event conversion framework. We empirically demonstrate the effectiveness of event-based representation over traditional frame-based methods in predicting these collective behaviors. Based on our analysis, we present event-based vision for Multi-Agent dynamic Prediction (evMAP), a deep learning architecture designed for real-time, accurate understanding of interaction strength and collective behavior emergence in multi-agent systems.
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