Project Grant FA23862514065

This $300,000 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will fund research by the Regents of the University of California at Riverside (UC Riverside) to develop trustworthy and resilient coordination mechanisms for multi-agent systems. The project aims to enable autonomous systems like drones, vehicles, robots, and smart infrastructure to reliably work together, even when facing uncertainty, failure, or...

This $300,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop trustworthy and resilient coordination mechanisms for multi-agent systems (MAS). The research will advance foundational knowledge in three key areas: (1) detecting and mitigating abnormal behaviors in cooperating agents, (2) managing adversarial or non-cooperative agents using game-theoretic and adversarial machine learning methods, and...

The National Science Foundation awarded Princeton University a $411,476 project grant under the Computer and Information Science and Engineering program (CFDA 47.070) to develop the theoretical foundations of trust-centered resilience for distributed coordination and optimization of multi-agent systems in the presence of adversaries. Specifically, the funding will support research from June 2022 to May 2026 to establish analytical frameworks and efficient exploitation of stochastic information...

The National Science Foundation (NSF) awarded a 5-year, $538,000 CAREER grant to the University of Maryland, College Park to support research on the foundations of dynamic multi-agent learning under information constraints. The project will bridge insights from control theory and machine learning to develop theoretical principles and algorithms for multi-agent learning in partially observable, dynamic environments. Key research thrusts include formalizing the role of information structures,...

This Project Grant from the National Science Foundation Office of Integrative Activities, under the Integrative Activities federal grant program (CFDA 47.083), provides $271,129 to support research at the New Mexico Institute of Mining and Technology to develop a new method for multi-agent control of robotic systems. The research focuses on incorporating density information to optimize transport-based multi-agent exploration and maximize efficiency for applications such as wildlife monitoring,...

This Project Grant award of $398,990.00 from the National Science Foundation's Computer and Information Science and Engineering (CISE) program, awarded on August 1, 2025, supports research by the University of California, Irvine (UCI) to develop theoretical foundations for multi-agent learning systems. The project aims to design computationally efficient algorithms that can reliably converge to equilibrium states in cooperative and competitive multi-agent environments, as well as address open...

This three-year, $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of California, Davis to develop trustworthy machine learning systems through adversarial robust reinforcement learning. Specifically, the award supports investigating potential vulnerabilities in reinforcement learning models and algorithms, developing robust RL approaches that mitigate impacts from adversarial attacks, and...

This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program ($199,481) supports research at George Mason University from June 2023 through May 2026 to develop an approach for improving service robot decision-making and behavior during deployment in unfamiliar environments. The award aims to facilitate fast and reliable online retraining and adaptation of robot planning despite missing world knowledge. The researchers will develop a...

This $232,159 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research at Carnegie Mellon University to optimize environments for large-scale multi-agent coordination. The key objectives are to (1) establish a foundational understanding of environment optimization for multi-agent coordination, and (2) develop innovative environment designs tailored for large-scale multi-agent applications like...

This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $356,488 to the Texas A&M Engineering Experiment Station (Tees) to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The research aims to produce scalable and computationally-efficient RL algorithms with rigorous convergence and complexity analysis for applications like interference management...