Introspective learning to build case-based reasoning (CBR) knowledge containers

Case Based Reasoning systems rely on competent case knowledge for effective problem-solving. However, for many problem solving tasks, notably design, simple retrieval from the case-base in not sufficient. Further knowledge is required to help effective retrieval and to undertake adaptation of the retrieved solution to suit the new problem better. This paper proposes methods to learn knowledge for the retrieval and adaptation knowledge containers exploiting the knowledge already captured in the case knowledge.