Proceedings of the Annual Meeting of the Cognitive Science Society

As case-based reasoners gain experience in a domain, they need to improve their case retrieval so that more useful cases are retrieved. One problem in doing this is that the reasoner who most needs to learn is least able to explain successes or failures. A second problem is that uncontrolled pursuit of an explanation could be very expensive. There are three keys to the approach presented. First, the student observes expert problem solving and sets up expectations for what the expert will do next. When expectations fail, the reasoner has its failure isolated to a single step, and the correct action for the situation has been provided. Second, if the student can retrieve part of a case that would have suggested a correct prediction, then that case snippet can be used to limit the explanation process, making the process more efficient. Third, when no explanation can be found, the reasoner resorts to empirical adjustment of feature importance.