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CPS: Small: Multi-Human Assisted Learning for Multi-Agent Systems using Intrinsically Generated Event-Related EEG Potentials

CPS：小型：使用内在生成的事件相关脑电图电位进行多智能体系统的多人辅助学习

基本信息

批准号：
1837369
负责人：
Raghupathy Sivakumar
金额：
$ 50万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837369&HistoricalAwards=false
关键词：
CPS Small Multi Human Assisted

项目摘要

The goal of this project is to allow humans to assist machine learning algorithms in a Cyber-Physical System. The approach involves the use of electroencephalogram (EEG) based brain waves of the human-in-the-loop to generate feedback for the learning algorithms. Machine learning solutions are particularly useful for the monitoring, instrumenting, and optimization of complex cyber physical systems (CPS). However, several important problems spanning domains such as natural language processing, automated language translation, and understanding what is in a scene or image remain beyond the scope of true machine learning algorithms, and require human participation. The project involves collaboration with Georgia Tech's Create-X and Venture Lab programs to commercialize the outcomes of the proposed research. This project considers recognizing the erroneous behavior of the machine intrinsically by the error related negativity (ERN) in the human EEG signals, which are then used as the reward function for the reinforcement learning (RL) algorithm of the machine to improve its intelligence. Another novel aspect of the research is the consideration of multiple humans-in-the-loop providing feedback that is used as a reward function by the learning algorithms embedded within the CPS. Finally, the project also is exploring the benefits of convergence time acceleration and deep learning techniques to extract features from EEG that are independent of canonical ERN component peaks. The research is evaluated using both a game proxy based analysis and experimental analysis within the context of real-world CPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目标是允许人类在网络物理系统中协助机器学习算法。该方法涉及使用基于脑电图（EEG）的人在回路中的脑电波来生成用于学习算法的反馈。机器学习解决方案对于复杂网络物理系统（CPS）的监控、仪表化和优化特别有用。然而，跨越自然语言处理、自动语言翻译和理解场景或图像中的内容等领域的几个重要问题仍然超出了真正的机器学习算法的范围，需要人类参与。该项目涉及与格鲁吉亚理工学院的X-X和风险实验室项目合作，将拟议研究的成果商业化。该项目考虑通过人类EEG信号中的错误相关负性（ERN）内在地识别机器的错误行为，然后将其用作机器的强化学习（RL）算法的奖励函数，以提高其智能。该研究的另一个新颖方面是考虑多个人在回路中提供反馈，该反馈被嵌入CPS中的学习算法用作奖励函数。最后，该项目还在探索收敛时间加速和深度学习技术的好处，以从EEG中提取独立于规范ERN分量峰值的特征。这项研究是使用基于游戏代理的分析和实验分析在现实世界的CPS的背景下进行评估。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。