Proceedings of the Annual Meeting of the Cognitive Science Society

The architecture of an intelligent agent must include components that carry out a wide variety of cognitive tasks, including perception, goal activation, plan generation, plan selection, and execution. In order to make use of opportunities to learn, such a system must be capable of determining which system components should be modified as a result of a new experience, and how lessons that aie appropriate for each component's task can be derived from the experience. We describe an approach that uses a self-model as a source of information about each system component. The model is used to determine whether a component should be augmented in response to a new example, and a portion of the model, component performance specifications, are used to determine what aspects of an example are relevant to each component and to express the details of the lessons learned in vocabulary that is appropriate to the component. W e show how this approach is implemented in the CASTLE system, which learns strategic concepts in the domain of chess.