CAREER: Game Theoretic Models for Robust Cyber-Physical Interactions: Inference and Design under Uncertainty

职业：稳健的网络物理交互的博弈论模型：不确定性下的推理和设计

• 批准号: 2336840
• 负责人: David Fridovich-Keil
• 金额: $ 60万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336840&HistoricalAwards=false
• 关键词: CAREER; Game; Theoretic; Models; Robust

The long-term goal of this project is to build flexible models and efficient algorithms for large-scale, multi-agent, and uncertain cyber-physical systems. In settings such as traffic management, for example, practitioners face fundamental challenges due to complex dynamics, hierarchical influence, noncooperative actors, and hard-to-model uncertainty. Strong simplifying assumptions have become essential: for instance, many theoretical models of road networks take the form of static, deterministic, and/or aggregative games. In these instances, static assumptions make it possible to predict the aggregate impact of decisions such as tolling on traffic patterns. However, neglecting temporal dynamics and feedback effects can lead city planners to make myopic decisions, which may have unintended consequences as drivers adapt to one another's behavior over time. This project develops theoretical and algorithmic techniques to address some of the underlying challenges and will also support mentoring of graduate and undergraduate researchers, development of undergraduate course material, and outreach to local underrepresented communities. This NSF CAREER project aims to develop a sound algorithmic basis for game-theoretic inference and design in dynamic and multi-agent CPS. The specific goals of this project are threefold. The first goal is to formalize and solve a set of structural inference problems in noncooperative games that arise in transportation. For example, one such problem is to discover hierarchies of influence among decision-makers from observations of their actions. The second goal of this project is to design dynamic, time-varying mechanisms which influence agents’ decisions and induce desired outcomes. In transportation systems, these mechanisms correspond to tolls, bus routes, timetables, etc. The third and final goal considers stochastic variants of the aforementioned games and aims to develop a computationally-tractable theory of time-varying, feedback decision-making in these settings. This project will enable the analysis and design of cyber-physical systems which interact with one another in complex hierarchies and enable planners and regulators to guide these systems toward desired outcomes. Theory and algorithms will be validated in a physical laboratory testbed which emulates urban driving, via large-scale simulation of traffic in the city of Austin and using French air traffic management data.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.

该项目的长期目标是为大规模、多智能体和不确定的网络物理系统构建灵活的模型和高效的算法，例如，在交通管理等环境中，从业者面临着由于复杂的动态而面临的根本挑战。 、等级影响、非合作参与者和难以建模的不确定性已变得至关重要：例如，道路网络的许多理论模型采用静态、确定性和/或聚合博弈的形式。假设使得预测总体影响成为可能然而，忽视时间动态和反馈效应可能会导致城市规划者做出短视的决策，随着时间的推移，司机会适应彼此的行为，这可能会产生意想不到的后果。该 NSF 职业项目旨在为博弈论推理和研究奠定良好的算法基础。该项目的具体目标有三个：形式化并解决非合作博弈中出现的一组结构推理问题，例如，其中一个问题是发现层次结构。该项目的第二个目标是设计动态的、随时间变化的机制，影响代理人的决策并产生期望的结果。在交通系统中，这些机制对应于通行费、公交路线、时刻表等第三个也是最后一个目标考虑了上述游戏的随机变体，旨在开发一种可计算处理的时变反馈决策理论，该项目将能够分析和设计与之交互的网络物理系统。理论和算法将在模拟城市驾驶的物理实验室测试台中通过对奥斯汀市交通的大规模模拟并使用法国空气进行验证。交通管理该奖项反映了 NSF 的法定使命，并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，被认为值得支持。