1999 | OriginalPaper | Chapter
Maintenance of Discovered Knowledge
Authors : Michal Pěchouček, Olga Štěpánková, Petr Mikšovský
Published in: Principles of Data Mining and Knowledge Discovery
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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The paper addresses the well-known bottleneck of knowledge based system design and implementation – the issue of knowledge maintenance and knowledge evolution throughout lifecycle of the system. Different machine learning methodologies can support necessary knowledge-base revision. This process has to be studied along two independent dimensions. The first one is concerned with complexity of the revision process itself, while the second one evaluates the quality of decision-making corresponding to the revised knowledge base. The presented case study is an attempt to analyse the relevant questions for a specific problem of industrial configuration of TV transmitters. Inductive Logic Programming (ILP) and Explanation Based Generalisation (EBG) within the Decision Planning (DP) knowledge representation methodology, have been studied, compared, and tested on this example.