Proceedings of the Annual Meeting of the Cognitive Science Society

AI research on explanation has not seriously addressed the influence of explainer goals on explanation construction. Likewise, psychological research has tended to assume that people's choice between explanations can be understood without considering the explainer's task. W e take the opposite view: that the influence of task is central to judging explanations. Explanations serve a wide range of tasks, each placing distinct requirements on what is needed in an explanation. W e identify eight main classes of reasons for explaining novel events, and show how each one imposes requirements on the information needed from an explanation. These requirements form the basis of dynamic, goal-based explanation evaluation implemented in the program ACCEPTER . We argue that goal-based evaluation of explanations offers three important advantages over static criteria: First, it gives a way for an explainer to know what to elaborate if an explanation is inadequate. Second, it allows cross-contextual use of explanations, by deciding when an explanation built in one context can be applied to another. Finally, it allows explainers to make a principled decision of when to accept incomplete explanations without further elaboration, allowing explainers to conserve processing resources, and also to deal with situations they can only partially explain.