Long Term Cooperation
Long-term cooperation, the sustained collaborative effort among agents towards shared goals, is a central research theme across diverse fields, aiming to understand its emergence and optimize its effectiveness. Current research focuses on developing algorithms and models, such as reinforcement learning, multi-agent systems, and large language models, to facilitate cooperation in various settings, including human-machine interaction, multi-robot systems, and distributed control. These advancements have significant implications for improving the efficiency and robustness of complex systems, from autonomous vehicle coordination to collaborative AI and human-centered design of assistive technologies.
Papers
Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Cathy Wu, Katherine Driggs-Campbell
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
Robert Loftin, Saptarashmi Bandyopadhyay, Mustafa Mert Çelikok
Vehicles Swarm Intelligence: Cooperation in both Longitudinal and Lateral Dimensions
Jia Hu, Nuoheng Zhang, Haoran Wang, Tenglong Jiang, Junnian Zheng, Feilong Liu
Space Domain based Ecological Cooperative and Adaptive Cruise Control on Rolling Terrain
Mingyue Lei, Haoran Wang, Lu Xiong, Jaehyun (Jason)So, Ashish Dhamaniya, Jia Hu