This $599,744 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research by Yale University to develop new theory and methods for analyzing, training, and designing complex, high-dimensional multi-agent games and machine learning systems. The key activities under this 3-year award include: Developing optimal uncoupled algorithms for computing and learning equilibria in high-dimensional concave games...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $227,984 to the Massachusetts Institute of Technology (MIT) to advance the understanding of equilibrium computation and learning dynamics in multi-agent systems and games. The key objectives are to: 1) Refine algorithms for learning dynamics in games using tools from nonlinear dynamical systems; 2) Investigate efficient computation of...
The National Science Foundation (NSF) awarded a $800,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Los Angeles (UCLA) - Office of Research Administration. The grant, titled "COLLABORATIVE RESEARCH: III: MEDIUM: VIRTUALLAB: INTEGRATING DEEP GRAPH LEARNING AND CAUSAL INFERENCE FOR MULTI-AGENT DYNAMICAL SYSTEMS", will fund the development of a virtual laboratory framework to model and predict the...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $237,999 to support research on developing game-theoretic algorithms for online planning in partially observable domains. The awardee, The Regents of the University of Colorado, doing business as the University of Colorado, will work to design decision-making algorithms based on game theory that enable autonomous agents to interact...
This $1,500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports the University of Southern California's research on safe multi-agent systems using a neurosymbolic approach. The project aims to develop new theories and algorithms for the design of safe learning-enabled multi-agent systems, with applications in areas like wildfire prevention using drone swarms and semi-automated...
The National Science Foundation (NSF) awarded a $425,225 CAREER grant under the Engineering program (CFDA 47.041) to the University of Maryland, College Park. The grant supports fundamental research on dynamic multi-agent learning in partially observable environments. The project aims to integrate insights from control theory and machine learning to develop theoretical foundations and practical algorithms for agents to learn effectively while operating under information constraints. Key research...
The National Science Foundation awarded a $499,835 Project Grant to George Washington University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) for the period of October 1, 2021 through September 30, 2024. The grant funds research titled "SMALL: HIGH-PERFORMANCE MULTI-AGENT REINFORCEMENT LEARNING" which will advance the development of multi-agent reinforcement learning techniques. As described under the CFDA program, the NSF aims to...
This National Science Foundation (NSF) Division of Information and Intelligent Systems Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $299,214 over 4 years to Oberlin College to conduct collaborative research on decision-making methods for open multi-agent systems. The research investigates how autonomous agents can make optimal decisions under various types of uncertainty, including changes to the system composition, tasks,...
The National Science Foundation Division of Computing and Communication Foundations awarded $800,000 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to the University of California, Berkeley for a four-year collaborative research project grant. The project aims to improve the sample efficiency of reinforcement learning algorithms in both offline and online settings through techniques like optimistic exploration and pessimistic exploitation. It...
This National Science Foundation Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $467,141 to the University of Georgia Research Foundation to investigate decision-making frameworks for open multi-agent systems with various forms of uncertainty. The research aims to develop novel planning and reinforcement learning techniques to enable agents to operate optimally in open contexts where the system composition, tasks, and agent...