Intelligently assessing relevance is a central issue in AI. Whether in diagnosis, problem solving, or explanation, AI systems must determine the information in a database that isrelevant to the task. Assessing relevance is difficult because it depends on context. This work focuses on resoning symbolically about relevance of cases or features in context in a computationally tractable way. Integrating expanded domain theories, where such theories are often ill-defined or partial, into the assessment of relevance is a primary goal of the work. The ultimate goal of this approach is to design an intelligent tutoring system that will train students to argue with cases.
