论文标题
自适应多机构连续学习系统
Adaptive Multi-Agent Continuous Learning System
论文作者
论文摘要
我们提出了一个自适应多代理聚类识别系统，可以根据具有适应性的时间序列的连续学习机制进行自学驱动。该系统旨在使用一些不同的功能代理来建立连接结构，以通过预测代理的输入来驱动代理来实现使用传统算法方法来实现序列识别序列的行为，以提高应对环境多样性的需求。最后，视频行为聚类的可行性实验证明了系统应对动态情况的可行性。我们的工作放在这里\ footNote {https://github.com/qian-git/mammals}。
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}.
