  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}.
