AUTONOMOUS SYSTEMS: Description and Construction E. von Goldammer 1 , C. Kennedy, J. Paul, H. Lerchner and R. Swik 1 To whom correspondence should be addressed: FH Dortmund, FB Informatik. published in: Cybernetics and Systems (R.Trapel, ed.), Vol.I, Proc.of XIII Europ. Meeting in Cybernetics and Systems Research, Wien, 1996, p.195-200. Abstract Adaptive and learning systems with high degrees of autonomy will be discussed from both a mono- contextural and a poly-contextural point of view. Staatistical learning algorithms as well as the new class of adaptive computational models such as neural networks, genetic algorithms, or fuzzy logic, which have recently been termed "soft" logical computation, are categorized in this paper as mono- contextural conceptions. Mono-contextural descriptions are always hierarchically structured, i.e., the triangle inequality as a defining relationship of metricity strictly holds. All input/output systems with (or without) implemented feedback algorithms belong to this category. On the other hand, all models of self-referential (cognitive) processes belong to the class of heterarchically structured descriptions which lead to logical antinomies and ambiguities if described in a mono-contextural logical framework. Introduction In a conventional sense, control is the capability of a system to present inputs to processes, plants or machines such that only the desired outputs or actions are observed. To illustrate the use of simple classification algorithms, the reception of pulse signals in a noisy background may be considered. An adaptive receiver must generate the decision rule that would classify the receiver signal (where signal and noise are mixed) into two classes where signal and noise are separated. The first question that must be asked for a control system of any kind is: what is it for – for what purpose will it be designed? It is usually a dangerous oversimplification to imagine that the answer is a single easily evaluated function which remains constant over time. A real-world control system should be able to respond to changing demands and situations and should be oriented towards satisfying many requirements at once. It should be able to handle complex tasks containing unpredictable events and changing environments within a given context of control; not only must the plant output be maintained within specified limits, but it must also be done cheaply, quickly, and efficiently. Adaptive control is a branch of control theory in which a controlled system is modeled, typically by means of a set of linear difference or differential equations, some of whose parameters are unknown and must be estimated. For the latter cases fuzzy logic and/or neural nets have become an attractive alternative in modern control theory. The application of the classical techniques of adaptive control theory, can be extremely powerful. However, its range of applicability is limited to tasks with very restrictive characteristics. These are characteristics of the system model and the ways in which it may be adapted. Although the assumptions of linearity are far broader than is commonly thought, many real-world tasks which are of scientific and/or industrial interest are excluded on the basis of model-dependent approaches. For many industrial control situations the necessity of classification of data or datasets does not exist. However, it becomes indispensible for some processes. Examples are: automated quality control and the construction of goal-seeking robots which are required to act in an unstructured (dynamically changing) environment. If automated quality control is required within an industrial production line, deviations from various standard situations or standard patterns must be detected and classified. It is then necessary to relate the measured sensor Winter-Edition 2005