Within the framework of so-called second generation expert systems [62] knowledge modeling is one of the most important aspects. On the one hand, knowledge acquisition is no longer seen as a knowledge transfer process, rather it is now considered as model construction process which is typically a cyclic and error prone process. On the other hand, the distinction between knowledge and symbol level descriptions [166] resulted in various proposals for conceptual knowledge models describing knowledge in an implementation independent way. One of the most prominent examples of such a conceptual model is the KADS model of expertise which is characterized by its clear distinction of different know ledge types and by the usage of specific modeling primitives to describe these different knowledge types [185]. The semi formal KADS expertise model entails all the advantages and disadvantages which have been identified for semi-formal system models e.g. in the software engineering community.
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The Knowledge Acquisition and Representation Language KARL is a formal and operational specification language for knowledge-based systems and second-generation expert systems. KARL provides language primitives to represent knowledge according to the layers of a KADS-oriented model of expertise. The main features of KARL are: it provides epistemologically adequate modelling primitives that allow knowledge specifications at the knowledge level. Therefore, KARL allows a smooth transition from informal to formal specifications. KARL is a formal knowledge specification language. That is, it has a declarative semantics. KARL is an operational knowledge specification language that allows prototyping, i.e. knowledge evaluation by testing.
Within the framework of so-called second generation expert systems [62] knowledge modeling is one of the most important aspects. On the one hand, knowledge acquisition is no longer seen as a knowledge transfer process, rather it is now considered as model construction process which is typically a cyclic and error prone process. On the other hand, the distinction between knowledge and symbol level descriptions [166] resulted in various proposals for conceptual knowledge models describing knowledge in an implementation independent way. One of the most prominent examples of such a conceptual model is the KADS model of expertise which is characterized by its clear distinction of different know ledge types and by the usage of specific modeling primitives to describe these different knowledge types [185]. The semi formal KADS expertise model entails all the advantages and disadvantages which have been identified for semi-formal system models e.g. in the software engineering community.
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Within the framework of so-called second generation expert systems [62] knowledge modeling is one of the most important aspects. On the one hand, knowledge acquisition is no longer seen as a knowledge transfer process, rather it is now considered as model construction process which is typically a cyclic and error prone process. On the other hand, the distinction between knowledge and symbol level descriptions [166] resulted in various proposals for conceptual knowledge models describing knowledge in an implementation independent way. One of the most prominent examples of such a conceptual model is the KADS model of expertise which is characterized by its clear distinction of different know ledge types and by the usage of specific modeling primitives to describe these different knowledge types [185]. The semi formal KADS expertise model entails all the advantages and disadvantages which have been identified for semi-formal system models e.g. in the software engineering community.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch. Nº de ref. del artículo: 9781461359593
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