Symbolic and qualitative reconstruction of control skill

Controlling a complex dynamic system such as a plane or a crane usually requires a skilled operator Such a control skill is typically hard to reconstruct through introspection There fore an attractive approach to the reconstruction of control skill involves machine learning from operators control traces also known as behavioural cloning In the most common approach to behavioural cloning a controller is induced as a direct mapping from system states to actions Unfortunately such controllers usually lack typical elements of human control strategies such as subgoals or desired trajectory and do not replicate the ro bustness of the human control skill In this paper we investigate a novel approach We apply the GoldHorn program to induce from the control traces a set of symbolic constraints Those con straints describe the operator s generalized trajectory and are then used together with locally weighted regression model to de termine the next action Using the crane problem in a case study this approach showed signi cant improvements both in terms of control performance and transparency of induced clones More over generalizing the trajectory into qualitative strategy shows the potentials of such an approach