A context model for knowledge-intensive case-based reasoning

Abstract

Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real-world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient or too brittle to handle new problems. In our research, we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem-solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem-solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained and an example shows its integration into an existing knowledge-intensive CBR system.