Coordinated Behavior

Coordinated behavior research investigates how multiple agents, whether robots, humans, or AI models, achieve shared goals through interaction and information exchange. Current research focuses on developing algorithms and architectures, such as reinforcement learning with various communication schemes (e.g., cheap talk, sequential communication) and neural coordination models, to improve efficiency and robustness of coordinated actions in diverse settings, including robotics, traffic management, and online social interactions. These advancements have implications for improving the performance of multi-agent systems in various applications and offer insights into the fundamental mechanisms underlying human and animal cooperation. Furthermore, understanding coordinated behavior enhances the interpretability and generalizability of multi-agent systems.