David B. Leake
When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build explanations, including both internal reasoning and external information search, and goal-based considerations have a profound effect on their choices of when and how to pursue explanations. However, standard AI models of explanation rely on goal-neutral use of a single fixed strategy---generally backwards chaining---to build their explanations. This paper argues that explanation should be modeled as a goal-driven learning process for gathering and transforming information, and discusses the issues involved in developing an active multi-strategy process for goal-driven explanation.