Collaborative Research: FMitF: Track I: Specification-Guided Multiagent Reinforcement Lear — NSF Award to Mississippi State Univer
Multi-agent systems (MAS) are pervasive with applications in various areas such as computer networks, robotics, and power grids. For example, multi-robot systems play a critical role in our society, including industrial robots in car assembly lines, hundreds of drones in a light show, and many vehicles in future autono
| Award title | Collaborative Research: FMitF: Track I: Specification-Guided Multiagent Reinforcement Lear |
|---|---|
| Award ID | 2525086 |
| Awardee | Mississippi State University |
| City | MISSISSIPPI STATE |
| State | MS |
| Amount obligated | $450,000 |
| Principal investigator | Chuangchuang Sun |
| Program | FMitF: Formal Methods in the F |
| Start date | 10/01/2025 |
| Abstract | Multi-agent systems (MAS) are pervasive with applications in various areas such as computer networks, robotics, and power grids. For example, multi-robot systems play a critical role in our society, including industrial robots in car assembly lines, hundreds of drones in a light show, and many vehicles in future autonomous ride-sharing services. Sequential decision-making is crucial to construct functional, intelligent MAS that can meet our needs. Multi-agent reinforcement learning is an approac |
| Source | NSF Awards |
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