Multi Agent Behavior

Multi-agent behavior research focuses on understanding and modeling the interactions and collective dynamics of multiple agents, aiming to predict and potentially influence their behavior. Current research emphasizes developing robust models, such as transformers and state space models, capable of handling diverse tasks like trajectory prediction, imputation, and behavior classification from various data sources, including video and aerial imagery. These advancements are crucial for applications ranging from autonomous driving and robotics to understanding animal behavior and improving human-AI interaction, particularly in addressing challenges related to fairness, interpretability, and robustness in multi-agent systems.