Machine-learned rule-based control

Machine-learned rule-based control differs from more typical approaches to the engineering of controllers for physical systems in the following respect. In traditional control theory, a mathematical model of the system is constructed and then analysed in order to synthesise a control method. This approach is clearly deductive. A machine-learning approach to the synthesis of controllers aims to inductively acquire control knowledge, thereby avoiding the necessity of constructing a mathematical model of the system. In applications where systems are very complex, or insufficient knowledge is available, the construction of such a model may be impossible, and traditional methods therefore inappropriate. It is for these applications that an inductive approach promises solutions.