Optimal Training System

Successful training in complex environments is normally accomplished through the interaction of a trainee and a skilled expert, but due to resource constraints, experts’ use in training can be problematic. Using an optimal model of task performance subject to human constraints may be a more efficient way to develop models of skilled human performance for use in training, especially since optimal models are simpler to validate, test, and debug than corresponding expert models. In addition, constrained optimal models can be constructed in domains where no experts are available or even exist. Using a simulated task environment (STE) permits the necessary close model-trainee interaction by enabling the construction of optimal performance models and by allowing them to perform the same task as the trainee using the same interface while closely observing and guiding trainee performance. We propose to extend our previous work using a normatively correct model of task performance as the core engine of an automated tutor for a national missile defense (NMD) task STE to the more complex domain of unmanned aerial vehicle (UAV) control and mission execution. This work will integrate the functionality of planning systems with human planning constraints obtained from a study of online classroom learning.