Abstract Systems operating in uncertain environments with minimum interaction with a human operator, may be managed by controls with special considerations. They result in a Hierarchically Intelligent Control System, the higher levels of which may be modeled as knowledge based system processing various types of information with Entropy as an analytic measure. The concept of Entropy of statistical Thermodynamics, is used to express the average value of the performance criterion of a feedback control, encountered at the lower levels of the system. Thus, the resulting optimal control problem may be recast as an information theoretic one, which minimizes the entropy of selecting the feedback controls. This unifies the treatment of all the levels of a Hierarchically Intelligent Control System by a mathematical programming algorithm which minimizes the sum of their entropies. The resulting "Intelligent Machine" is composed of three levels hierarchically ordered in decreasing intelligence with increasing precision: the organization level, performing information processing tasks like planning, decision making, learning and storage and retrieval of information from a long-term memory; the coordination level, dealing again with information processing tasks like learning, lower level decision making and dealing with short-term memory only and the control level, which performs the execution of various tasks through hardware using feedback control methods.
Intelligent control-operating systems in uncertain environments
1985-01-01
22 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch