Controlling a complex dynamic system, such as a plane or a crane, usually requires a skilled operator. Such control skill is typically hard to reconstruct through introspection. Therefore an attractive approach to the reconstruction of control skill involves machine learning from operator's control traces, also known as behavioral cloning. In the most common approach to behavioral cloning, a controller is induced as a direct mapping from system states to actions. Unfortunately, such controllers usually suffer from lack of robustness and lack typical elements of human control strategies, such as subgoals and substages of the control plan. We investigate a novel approach. We apply the GoldHorn program to induce from the operator's trajectories a set of symbolic constraints. These are then used together with a locally weighted regression model to determine the next action. Using the Acrobot problem in a case study, this approach showed significant improvements both in terms of control performance and transparency of induced clones.