CPS: Small: Collaborative Research: Distributed Coordination of Agents For Air Traffic Flow Management

CPS：小型：协作研究：空中交通流量管理代理的分布式协调

• 批准号: 0931591
• 负责人: Kagan Tumer
• 金额: $ 37万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0931591&HistoricalAwards=false
• 关键词: CPS; Small; Collaborative; Research; Distributed

CPS: Small: Collaborative Research: Distributed Coordination of Agents For Air Traffic Flow ManagementThis objective of this proposal is to improve the management of the air traffic system, a cyber-physical system where the need for a tight connection between the computational algorithms and the physical system is critical to safe, reliable and efficient performance. The approach is based on an adaptive multiagent coordination algorithm with a particular emphasis on the systematic selection of the agents, their actions and the agents' reward functions. The intellectual merit lies in addressing the agent coordination problem in a physical setting by shifting the focus from ``how to learn" to ``what to learn." This paradigm shift allows a separation between the learning algorithms used by agents, and the reward functions used to tie those learning systems into system performance. By exploring agent reward functions that implicitly model agent interactions based on feedback from the real world, this work aims to build cyber-physical systems where an agent that learns to optimize its own reward leads to the optimization of the system objective function. The broader impact is in providing new air traffic flow management algorithms that will significantly reduce air traffic congestion. The potential impact cannot only be measured in currency ($41B loss in 2007) but in terms of improved experience by all travelers, providing a significant benefit to society. In addition, the PIs will use this project to train graduate and undergraduate students (i) by developing new courses in multiagent learning for transportation systems; and (ii) by providing summer internship opportunities at NASA Ames Research Center.

CPS：小：合作研究：空中交通流量管理代理的分布式协调本提案的目标是改善空中交通系统的管理，这是一个网络物理系统，其中计算算法和物理系统之间的紧密连接对于安全、可靠和高效的性能至关重要。该方法是基于自适应多代理协调算法，特别强调系统的选择代理，他们的行动和代理的奖励功能。智能的优点在于通过将焦点从“如何学习”转移到“学习什么”来解决物理环境中的代理协调问题。“这种范式转变允许代理使用的学习算法与用于将这些学习系统与系统性能联系起来的奖励函数之间的分离。通过探索代理奖励功能，隐式模型代理互动的基础上从真实的世界的反馈，这项工作的目的是建立网络物理系统中的代理，学会优化自己的奖励导致系统目标函数的优化。 更广泛的影响是提供新的空中交通流量管理算法，将大大减少空中交通拥堵。潜在的影响不仅可以用货币来衡量（2007年损失了410亿美元），还可以用所有旅行者的体验改善来衡量，为社会带来重大利益。此外，PI将利用该项目培训研究生和本科生（i）通过开发运输系统多智能体学习的新课程;以及（ii）通过在NASA艾姆斯研究中心提供暑期实习机会。