Agent Behavior

Agent behavior research focuses on understanding and modeling how individual agents, whether simulated or real-world entities, act and interact within their environments. Current research emphasizes developing robust and explainable models, often employing reinforcement learning, large language models, and graph neural networks, to predict agent actions and analyze emergent behaviors in diverse settings like autonomous driving and human-robot collaboration. This field is crucial for improving the safety and reliability of autonomous systems, enhancing the design of multi-agent systems, and providing valuable insights into complex social and economic phenomena.