We propose an adaptive multi-agent clustering recognition system that can be self-supervised driven, based on a temporal sequences continuous learning mechanism with adaptability. The system is designed to use some different functional agents to build up a connection structure to improve adaptability to cope with environmental diverse demands, by predicting the input of the agent to drive the agent to achieve the act of clustering recognition of sequences using the traditional algorithmic approach. Finally, the feasibility experiments of video behavior clustering demonstrate the feasibility of the system to cope with dynamic situations. Our work is placed here\footnote{https://github.com/qian-git/MAMMALS}.

翻译：我们提出一种自适应多智能体聚类识别系统，该系统可通过自监督驱动，基于具有适应性的时间序列连续学习机制运行。系统设计采用多种不同功能型智能体构建连接结构，通过预测智能体输入驱动其完成序列聚类识别的传统算法行为，从而提升对多样化环境需求的适应能力。最后，视频行为聚类可行性实验验证了该系统应对动态场景的可行性。本工作代码托管于\footnote{https://github.com/qian-git/MAMMALS}。