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
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems
Nayana Dasgupta, Mirco Musolesi
Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations
Gösta Stomberg, Henrik Ebel, Timm Faulwasser, Peter Eberhard
Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Víctor Elvira, Jens Lagergren
Combining Theory of Mind and Abduction for Cooperation under Imperfect Information
Nieves Montes, Nardine Osman, Carles Sierra