Proceedings of the Annual Meeting of the Cognitive Science Society

Over the past few years there has been a growing interest in the notion of using causal explanations in both learning ([Dejong and Mooney, 1986] [Mitchell et a/., 1986]) and planning ([Hammond, 1989], [Hammond, 1987] and [Simmonsand Davis, 1987]). The study of how complex causal explanations can be used in learning has turned into something of a cottage industry in AI; however, little attention has paid to how explanations may be constructed. In this paper, we will examine some of the current proposals concerning the process of explanation, augment them with a few ideas of our own, and suggest a new, more strategic level of knowledge about explanations that can be used to guide the explanation process. In particular, we are interested in the problems involved with integrating rule-based methods of explanation construction with memory-based approaches.