Simulator-Mediated Acquisition of a Dynamic Control Skill

Abstract Uses of stored skill-models to accelerate simulator-based real-time training in a control skill are discussed. A real-time coach must deliver advice at three levels: (1) what to do next, (2) what to watch for, and (3) what went wrong. Human learning and machine learning results are presented using different screen representations of a pole-and-eart balancing task.