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AF: Small: Towards Theoretical Foundations of Multi-agent Learning - Algorithms, Dynamics — NSF Award to University of California-

This research project explores fundamental principles underlying how groups of intelligent decision-makers—called "agents"—interact and learn within shared environments. Understanding these interactions is increasingly important because they directly impact critical areas such as autonomous driving, economics, evolutio

Award titleAF: Small: Towards Theoretical Foundations of Multi-agent Learning - Algorithms, Dynamics
Award ID2454115
AwardeeUniversity of California-Irvine
CityIRVINE
StateCA
Amount obligated$398,990
Principal investigatorIoannis Panageas
ProgramAlgorithmic Foundations
Start date08/01/2025
AbstractThis research project explores fundamental principles underlying how groups of intelligent decision-makers—called "agents"—interact and learn within shared environments. Understanding these interactions is increasingly important because they directly impact critical areas such as autonomous driving, economics, evolutionary biology, robotics, artificial intelligence safety, and strategic decision-making. By developing theoretical insights and efficient learning algorithms, the project aims to det
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