This chapter is concerned with plants described by a knowledge representation in the form of relations with unknown parameters. The learning process consists here in step by step knowledge validation and updating [20, 28, 32, 34, 37, 51, 54]. At each step one should prove if the current observation “belongs” to the knowledge representation determined before this step (knowledge validation) and if not — one should modify the current estimation of the parameters in the knowledge representation (knowledge updating). The results of the successive estimation of the unknown parameters are used in the current determination of the decisions in a learning decision making system. This approach may be considered as an extension of the known idea of adaptation via identification for the plants described by traditional mathematical models (see e.g. ). We shall consider two versions of learning systems. In the first version the knowledge validation and updating is concerned with the knowledge of the plant (i.e. the relation R describing the plant), and in the second version — with the knowledge of the decision making (i.e. the set of decisions D u ). In both versions the learning algorithms based on the knowledge validation and updating will be presented.
KeywordsProbability Density Successive Estimation Learn System Recursive Algorithm Uncertain System
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