Learning from examples for the construction of fuzzy evaluations

Many concepts of knowledge-based problem-solving are incorporated into one system which has been based on set-theoretic concepts. This results in a methodology and in a comprehensive set of tools applicable in many fields. The transition between fuzzy and non-fuzzy parts of the problem domain can be realized very flexibly, supplying a high smartness and high performance model. The paper has the intention to show how to unify many problem-solving methods on the basis of set theoretic concepts and how to handle precise and fuzzy notions concepts as flexible as possible.<>