Autonomous Decision Making

Autonomous decision-making research focuses on developing intelligent systems capable of making optimal choices in complex and uncertain environments, prioritizing safety and efficiency. Current research heavily utilizes reinforcement learning (including deep reinforcement learning), Bayesian methods, and large language models to create adaptable and explainable agents for diverse applications, from space exploration and air traffic control to autonomous driving and industrial process optimization. This field is crucial for advancing robotics, AI safety, and various sectors requiring reliable and efficient autonomous systems, with ongoing efforts to improve model transparency, scalability, and human-robot collaboration.