Maintenance of discovered knowledge

Abstract

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.