Federal Grant Award Summary

The National Science Foundation (NSF) Directorate for Engineering awarded the University of Texas at Arlington $299,963 on July 15, 2026, through its Engineering program (CFDA 47.041) to support collaborative research on scaling robust multi-agent systems. The project, which runs through June 30, 2029, will develop efficient learning methods and decentralized algorithms that enable autonomous multi-agent systems to perform reliably in real-world environments. The primary deliverables include foundational theoretical advances in robust multi-agent learning with mathematical convergence guarantees, new function approximation frameworks to address dimensionality challenges, and practical robust control methods validated on intelligent transportation systems (ITS) and power grid applications.

Beyond core research outputs, the project will advance trustworthy artificial intelligence (AI) for critical national infrastructure by releasing open-source software tools, integrating research findings into university curricula, and organizing workshops with industry and government partners to inform certification standards for autonomous systems. By bridging the theoretical-practical divide in multi-agent learning, the work directly addresses the performance degradation that occurs when AI systems trained in simulations are deployed in noisy, variable real-world conditions—a critical challenge for systems where errors among multiple interacting agents can propagate and compound across entire networks.