Proceedings of the Annual Meeting of the Cognitive Science Society

How does working within a unified theory of cognition - an architecture - provide useful constraint when modeling large timescale tasks, where performance Is primarily determined by knowledge, rather than the architecture's basic mechanisms? W e present a methodology for extracting the constraint that comes from the architecture, by deriving a set of architectural entailments which as for certain model properties over others. The methodology allows us to fzictor the effect that various architectural properties have on a model. We demonstrate the methodology with a case study: a model of learning procedures from natural language instructions, Instructo-Soar, within the Soar architecture.