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有效的算法。例如，在交通管理等环境中，由于复杂的动态、等级影响、不合作的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。强有力的简化假设已经变得至关重要：例如，许多道路网络的理论模型采取静态，确定性和/或聚合游戏的形式。在这些情况下，静态假设使得有可能预测诸如收费等决策对交通模式的总体影响。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定，这可能会产生意想不到的后果，因为驾驶员会随着时间的推移而适应彼此的行为。该项目开发了理论和算法技术，以解决一些潜在的挑战，并将支持研究生和本科生研究人员的指导，本科课程材料的开发，并推广到当地代表性不足的社区。这个NSF CAREER项目旨在为动态和多代理CPS中的博弈论推理和设计开发一个良好的算法基础。该项目的具体目标有三个方面。第一个目标是形式化和解决一组结构推理问题的非合作博弈中出现的运输。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层次。这个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，并诱导预期的结果。在交通系统中，这些机制对应于收费，巴士路线，时间表等第三个和最后一个目标考虑上述游戏的随机变量，旨在开发一个计算易处理的理论随时间变化，反馈决策在这些设置。该项目将使网络物理系统的分析和设计，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引导这些系统实现预期的结果。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NSF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.