We describe our research toward building systems that include a complex, multi-state model of human dynamic behavior. This can allow us to predict human behavior over short periods of time, in order to create control systems that intelligently complement the human's action. To accomplish this requires inferring the internal state of the human, and then correctly adapting the remainder of the system to achieve optimal performance. We describe methods for achieving this goal, and report an initial experiment in which we were able to achieve 95\% accuracy at predicting automobile driver's actions from their initial preparatory movements.