LEARNING DESIGN KNOWLEDGE AS GENERALIZATION

Generalizing design knowledge is a process of extracting knowledge fro m design-provided data. Although it is one of the most common ways of ''learning'' design knowledge, generalization has a fundamental weakne ss: except for special occasions, the results of generalization can ne ver be validated. Inquiries into generalization have therefore dealt w ith questions of what are the best criteria for guiding the generaliza tion. This paper argues that generalizing design knowledge involves th ree essential tasks; knowledge representation, a description language for design examples, and generalization operators. An application of g eneralizing empirical networks from design examples is described to il lustrate these three tasks in the development of a generalization syst em.