基于硬注意力机制下的鱼群涌现自动建模方法

生物集群的协同智能可用于启发人工复杂系统调控，但是现有的自动建模方法往往不符合生物集群信息的处理特点，导致单体的信息交互建模仍充满挑战。不失一般性，借助红鼻剪刀鱼的集群运动数据设计符合生物硬注意力机制的深度网络模型，该结构能强制单体考虑至多两个以内的邻居信息，并能显现出高影响力邻居经常出没的隐藏位置，说明硬注意力模型符合生物集群的信息处理机制。实验结果表明：所提硬注意力模型具有较为良好的稀疏信息解耦能力、较为鲁棒的集群运动指标以及较为优秀的集群规模泛化性能，为复杂系统的多层次行为分析提供了有力的工具支撑，该方法对集群机器人的分布式控制具有较强的启发意义。

Automatic modeling method of fish schooling emergence based on hard attention mechanism

The study of biological collaborative intelligence can be used to inspire the regulation of artificial complex systems. However, most existing automated modelling approaches do not match the characteristics of biological collective information processing. The information mechanisms for modelling individual interaction are still full of challenges. Without loss of generality, a deep network model conforming to the biological hard attention mechanism was designed based on the collective motion data of hemigrammus rhodostomus. The designed model structure forced the individual to consider less than two neighbors information. In spite of such little information, the model could reveal the hidden place, where the high-impact neighbors located frequently. Thus, the hard attention model coincided with the information processing mechanism of biological collective interaction. The experimental results showed that the proposed hard attention model had better sparse information decoupling capability, more robust collective motion metrics and better collective scale generalization performance. The proposed model provides a powerful tool to support multi-levels behavior analysis of complex systems. Moreover, this method had strong inspirational significance for distributed controller design of swarm robotics.