D. Fridovich-Keil is a recipient of a CAREER Award from the NSF’s CPS Program to study models and algorithms for large-scale, multi-agent, and uncertain cyber-physical systems.
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 cyber-physical systems. 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.
Most prior work in multi-agent cyber-physical systems is restricted to highly structured settings, such as static routing games in which rational actors possess full information. Yet, with the advent of (semi)autonomous traffic and app-informed route guidance, these assumptions have become brittle. This project emphasizes the dynamic, time-varying nature of cyber-physical interactions as a first-class citizen, and considers settings in which critical aspects of agents’ decision-making processes are unknown, open to external influence, and/or stochastic. This necessitates a careful analysis of time-varying incentive structures, feedback effects, and uncertainty representations, as well as an investigation of efficient numerical algorithms. This project will build fundamental frameworks for modeling, control, and mechanism design applicable to a wide range of cyber-physical systems, including transportation networks and smart grids, and ultimately pave the way for new and exciting inquiries into, e.g., multi-agent cyber-physical systems’ sensitivity to active deception or varied structures of hierarchical influence.
This project has the potential to reduce fossil fuel consumption and improve transportation safety by improving the reliable flow of ground and air traffic. The innovations of this project can also extend beyond transportation, e.g., by making our energy grid more robust to environmental shocks. An educational objective of this project is to expose undergraduate and graduate students at UT Austin to the subtle and often unanticipated consequences of multi-agent interactions, ranging from pilot-induced oscillations to traffic jams. To that end, this project aims to build interdisciplinary connections between students and officials at the city of Austin’s expanded public transit network, Project Connect.