Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence

The notion of cyber-physical systems (CPS) describes the co mbination of Information and Communication Technology (ICT) and software (the "cyber" part) with physical compone nts. A CPS can emerge from embedded systems by internetworking them. The first big research program focusi ng on CPS has been started by the US National Science Foundation in 2006, where the term CPS is defined in as such tha t it "refers to the tight conjoining of and coordination between computational and physical resources," stating "[ w]e envision that the cyber-physical systems of tomorrow will far exceed those of today in terms of adaptability, auto nomy, efficiency, functionality, reliability, safety, and usability" [1]. While the notion of CPS by the U.S. National Science Foundati on, as outlined above, includes ICT, it does not explicitly name Artificial Intelligence (AI) as a necessary component to raise an embedded system to the status of a CPS. Y et, the availability of sensory data together with a co mmunications system and the ability to exert actions upon the physical world that have been planned for the whole compo und of embedded systems components readily suggests that issues of planning, the increase of reflectivity, effici ency, and lowering resource usage is achieved by increasing the "intelligence" of the overall system. As such, research ers in the domain of AI have found numerous application domains. However, the two worlds of CPS and AI usually operate on diffe rent terms: CPS require operation within well-defined boundaries, i.e., as far as possible deterministic behavio r within well-known, strictly enforced margins of error. In contrast, many AI techniques--Artificial Neural Networks (A NNs) foremost--are firmly rooted in the domain of statistics, which is probably very well seen in the ANN train ing process.