Knowledge Acquisition and Learning by Experience - The Role of Case-Specific Knowledge

As knowledge-based systems are addressing increasingly complex domains, their roles are shifting from classical expert systems to interactive assistants. To develop and maintain such systems, an integration of thorough knowledge acquisition procedures and sustained learning from experience is called for. A knowledge level modeling perspective has shown to be useful for analyzing the various types of knowledge related to a particular domain and set of tasks, and for constructing the models of knowledge contents needed in an intelligent system. To be able to meet the requirements of future systems with respect to robust competence and adaptive learning behavior, particularly in open and weak theory domains, a stronger emphasis should be put on the combined utilization of casespecific and general domain knowledge. In this chapter we present a framework for integrating KA and ML methods within a total knowledge modeling cycle, favoring an iterative rather than a top down approach to system development. Recent advances in the area of case-based reasoning provide a suitable basis. Focus is put on the knowledge maintenance part, for which a case-based method for learning from experience is described and exemplified by existing systems. Our own approach to integration of casespecific and general domain knowledge (the CREEK system) is briefly sketched, and used as a context for discussing case-based approaches to knowledge modeling in general.