Maintenance of discovered knowledge

摘要

The paper addresses the well-known bottleneck of knowledge based ssytem 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 prsented 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.