The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

This 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.

This 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 multi-agent 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 multi-agent learning for transportation systems; and (ii) by providing summer internship opportunities at NASA Ames Research Center.