Learning Coordination for Multi-Autonomous Multi-Human (MAMH) Agent Systems with Guaranteed Safety

具有安全保证的多自主多人（MAMH）代理系统的学习协调

• 批准号: 2332210
• 负责人: Xuan Wang
• 金额: $ 34.46万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332210&HistoricalAwards=false
• 关键词: Learning; Coordination; Multi; Autonomous; Human

The operation of many real-world systems involves the co-existence of human and autonomous agents. Inadequate coordination among these agents can lead to significant performance degradation or safety risks. This project aims to develop a novel framework for Multi-Autonomous Multi-Human coordination, which enhances algorithmic scalability and safety guarantees. Compared with traditional optimization and machine learning approaches, the proposed framework addresses two major challenges: (i) the non-cooperative nature of the system, which arises from information asymmetry between humans and robots, heterogeneity in human preferences, and human selfishness in decision-making when working with robots; and (ii) coordination safety, which is of critical importance in the presence of human agents but is difficult to measure using traditional black-box learning models. Additionally, human behaviors are subject to uncertainties, which may easily deviate the actual coordination from intended ones.To address these challenges, the intellectual merits of this research lie in its innovative integration of game theory, machine learning, human modeling, and network control theory, resulting in a framework for Multi-Autonomous Multi-Human coordination that enhances both model transparency and learnability. Core to the framework is a novel human-response alignment mechanism, allowing autonomous agents in the system to not only passively adapt to human behaviors but also subtly guide them, enhancing the efficiency and safety of the entire system. To facilitate this, computationally scalable and efficient algorithms will be developed in the manner of distributed-training-distributed execution, purely based on agents’ local resources for communication and computation. The broader impacts of this work extend to various engineering practices, including traffic coordination, human-robot teaming, and power/IoT systems involving human users. The project has a special emphasis on workforce development and education. A carefully designed "RoboArt" event will engage K-12 students, fostering creativity, problem-solving skills, and STEM exposure. The project will also offer multidisciplinary learning and research opportunities for high school and university students, ensuring inclusive access to the evolving field of robotics and machine learning. Furthermore, the project will contribute valuable datasets to the research community, emphasizing accessibility and re-usability to facilitate ongoing innovation in the field.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.

许多现实世界系统的运行涉及人类和自治药物的共存。这些试剂之间的协调不足会导致绩效降低或安全风险。该项目旨在为多自治的多人类协调开发一个新颖的框架，从而增强了算法可扩展性和安全性保证。与传统的优化和机器学习方法相比，所提出的框架解决了两个主要挑战：（i）系统的不合作性质，这是由人与机器人之间的信息不对称，人类偏好的异质性以及与机器人合作时的决策制定中的人类自私； （ii）协调安全，这在人类代理的存在下至关重要，但很难使用传统的黑盒学习模型来衡量。另外，人类行为受到不确定性的影响，这可能很容易将实际协调偏离预期的协调。为了应对这些挑战，这项研究的智力优点在于其游戏理论，机器学习，人类建模和网络控制理论的创新整合，从而为多自治的多人协调增强了模型跨跨跨性跨性别的框架。框架的核心是一种新颖的人类响应对准机制，使系统中的自主剂不仅可以被动地适应人类的行为，而且可以巧妙地指导它们，从而提高了整个系统的效率和安全性。为了促进这一点，将以分布式培训分布的执行方式开发计算上可扩展和有效的算法，纯粹基于代理的本地资源来进行通信和计算。这项工作的更广泛影响扩展到了各种工程实践，包括交通协调，人机团队以及涉及人类用户的力量/物联网系统。该项目特别着重于劳动力发展和教育。精心设计的“ Roboart”活动将吸引K-12学生，促进创造力，解决问题的技能和STEM暴露。该项目还将为高中和大学生提供多学科的学习和研究机会，以确保访问机器人技术和机器学习不断发展的领域。此外，该项目将为研究界贡献宝贵的数据集，强调可访问性和可重复使用能力，以支持该领域的持续创新。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估来获得的支持。