Common ground and differences of the KADS and strong-problem-solving-shell approach

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 599)


Model-based knowledge acquisition seems to be one obvious way to make the development of expert systems more effective and in particular helps to overcome the knowledge acquisition bottleneck. In this paper two approaches for model-based knowledge acquisition are compared: 1) the KADS approach offering generic conceptual models (Interpretation Models) as well as a language to describe, adapt and construct conceptual models and 2) the shell approach providing predefined and operationalized models of problem solving methods combined with a computer environment to acquire the domain knowledge, desirably with graphical knowledge editors, to apply it to a case and to explain the problem solving behavior. To get a better insight to KADS, the cognitive model of a well-established shell for heuristic classification is studied and expressed in terms of KADS. Strengths and weaknesses of the both approaches are pointed out as well as hints given for their mutual fertilization.


Expert System Knowledge Representation Knowledge Acquisition Knowledge Source Knowledge Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  1. 1.Institute of LogicKarlsruhe UniversityKarlsruheGermany

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