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

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建立灵活的模型和高效的算法。例如，在交通管理等环境中，由于复杂的动态、等级影响、非合作行为者和难以建模的不确定性，从业者面临着根本性的挑战。强大的简化假设变得至关重要：例如，许多道路网络的理论模型采用静态、确定性和/或聚合博弈的形式。在这些情况下，静态假设使预测诸如收费对交通模式等决策的总体影响成为可能。然而，忽视时间动态和反馈效应可能导致城市规划者做出短视的决定，这可能会产生意想不到的后果，因为司机会随着时间的推移适应彼此的行为。该项目开发理论和算法技术来解决一些潜在的挑战，还将支持对研究生和本科生研究人员的指导，开发本科课程材料，并向当地代表性不足的社区提供服务。这个NSF CAREER项目旨在为动态和多智能体CPS的博弈论推理和设计开发一个健全的算法基础。该项目的具体目标有三个方面。第一个目标是形式化并解决交通运输中出现的非合作博弈中的一组结构推理问题。例如，其中一个问题是通过观察决策者的行为来发现他们之间的影响等级。该项目的第二个目标是设计动态的、时变的机制来影响智能体的决策并诱导期望的结果。在运输系统中，这些机制对应于收费、公交路线、时刻表等。第三个也是最后一个目标是考虑上述游戏的随机变量，并致力于在这些设置中开发一种可计算的时变反馈决策理论。该项目将能够分析和设计网络物理系统，这些系统在复杂的层次结构中相互作用，并使规划者和监管者能够指导这些系统走向预期的结果。理论和算法将在模拟城市驾驶的物理实验室测试平台上进行验证，通过大规模模拟奥斯汀市的交通并使用法国空中交通管理数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.