5 fish experimental data

Collective intelligence in biological groups can be employed to inspire the control of artificial complex systems, such as swarm robotics. However, modeling for the social interactions between individuals is still a challenging task. Without loss of generality, we propose a deep attention network model that incorporates the principles of biological Hard Attention mechanisms, that means an individual only pay attention to one or two neighbors for collective motion decision in large group. The model is trained by the collective movement data of 5 rummy-nose tetra fish (Hemigrammus rhodostomus). The structure of the model enforces individual agents to consider information from at most two neighboring agents. Meanwhile, the model can reveal hidden locations, where highly influential neighbors frequently appear. These findings demonstrate that the proposed Hard Attention model aligns with the information processing mechanisms, which is observed in fish schooling. Experimental results indicate that the model exhibits a strong ability to decouple sparse information for collective movement with robust metrics. It can also perform excellent scalability in different group sizes. The simulation and real robots experiment show that the model provides a powerful tool for analyzing multi-level behaviors in complex systems and offers significant insights for the distributed control of swarm robotics.

生物群体中的集体智慧可被应用于启发人工复杂系统的控制，例如群体机器人技术。然而，对个体间社会互动进行建模仍是一项极具挑战的任务。不失一般性，我们提出了一种融合生物硬注意力机制原理的深度注意力网络模型，即个体仅对一到两个邻近个体在大型群体中的集体运动决策给予关注。该模型通过5条红鼻鱼（Hemigrammus rhodostomus）的集体运动数据进行了训练。模型结构强制个体智能体仅考虑最多两个邻近智能体的信息。同时，该模型能够揭示频繁出现的高度影响性邻居的隐藏位置。这些发现表明，所提出的硬注意力模型与鱼类集群中观察到的信息处理机制相吻合。实验结果表明，该模型展现出强大的解耦稀疏信息以实现集体运动的能力，并具有稳健的度量指标。此外，该模型在不同群体规模上表现出卓越的可扩展性。仿真和真实机器人实验表明，该模型为分析复杂系统中的多级行为提供了强大的工具，并为群体机器人技术的分布式控制提供了重要的洞见。