There may be the temptation to classify the area of intelligent autonomous systems as simply a collection of methods and ideas already addressed elsewhere, the need only being some kind of intelligent assembly and integration of known techniques. This is of course not true. The theory of control systems is not covered by say the area of applied mathematics, because control has different needs and therefore asks different questions. For example, while in applied mathematics the different solutions of differential equations under different initial conditions and forcing functions are of interest, in control one typically is interested in finding the forcing functions that generate solutions, that is system trajectories, that satisfy certain conditions. This is a different problem, related to the first, but its solution requires the development of quite different methods. In a rather analogous fashion the problems of interest in intelligent systems require development of novel concepts, approaches and methods. In particular while computer science typically deals with static systems and no real-time requirements, control systems typically are dynamic and all control laws, intelligent or not, must be able to control the system in real time. So in most cases one cannot really just directly apply computer science methods to these problems. Modifications and extensions are typically necessary for example in the quantitative models used to study such systems. And although say Petri nets may be adequate to model and study the autonomous behavior at certain levels of the hierarchy, these models may not be appropriate to address certain questions of importance to control systems such as stability, without further development and modifications. In addition, there are problems in intelligent autonomous control systems that are novel and so they have not studied before at any depth. Such is the case of hybrid systems for example that combine systems of continuous and discrete state. The marriage of all these fields can only be beneficial to all. Computer science and operation research methods are increasingly used in control problems, while control system concepts such as feedback, and methods that are based on rigorous mathematical framework can provide the base for new theories and methods in those areas.