9511504 HUMMEL This research will develop a computer system to model how people learn schemas from examples. Schemas are knowledge structures that describe general situations, events, rules, or relationships. For example, a Restaurant Schema might specify the kinds of events that take place in restaurants, and the relationships between the actors in those events (e.g., chefs, waiters, and customers, etc.); a Family Relation Schema might specify the kinds of relationships that define various family members, such as sisters, brothers, parents, uncles, etc. A schema is very general, in that it refers to many specific situations, and also highly structured, in that it specifies the relationships between objects, events, or even between other relationships. Psychologists and computer scientists have long recognized the utility of schemas as a basis for reasoning: If one can recognize an object or situation as an instance of a general schema, then one can use the schema to reason about the specific object or situation. For example, the Family Relation Schema tells us to expect that Mom's brother, Bob, is our uncle, and that if Bob has children, then they will be our cousins. Due to the schema, it is not necessary to learn the family relations for every individual separately. Although the generality and structured nature of schemas make them extremely useful forms of knowledge, they also make them very difficult to model: Traditional symbolic approaches to computer modeling are good at representing structured information, but they have difficulty flexibly matching specific instances to general facts; by contrast, connectionist models are good at flexibly matching instances to general categories, but they have great difficulty representing structured information (such as rules and relationships). Perhaps for this reason, no one has ever developed a formal and general model of schema-based learning and reasoning. Hummel and Holyoak have developed a computer mo del of analogy that combines a connectionist architecture with the capacity to represent and learn relational structures. The model's capacity to represent structured information in a flexible, general manner makes it an ideal vehicle for simulating human schema learning and use. The research will apply the model's approach to the representation of structure to the problems of representing, learning, and using schemas. The result will be a model that can (a) learn general schemas from specific examples, (b) match new instances to learned schemas, and (c) use the schemas to make inductive inferences about the new examples. The model will be the first formal theory of human schema-based reasoning, and will contribute substantially to our understanding or human reasoning is a wide variety of domains. It will also be the first working computer system to use schemas to reason flexibly about general knowledge domains. ***
