Team Affiliation

Team affiliation research explores how groups of agents, whether human or artificial, collaborate to achieve shared goals, focusing on optimizing team composition, predicting performance, and understanding team dynamics. Current research employs diverse approaches, including probabilistic graphical models to analyze team collapse, multi-task learning for efficient agent representation, and submodular optimization for selecting effective agent ensembles. This work has implications for diverse fields, from improving sports analytics and robotics to enhancing the design of artificial intelligence systems and understanding human-robot collaboration.