Acquiring design knowledge from design cases

Single-purpose learning algorithms are often not efficient in acquiring knowledge for expert systems in design. The authors present a method that learns design knowledge from design cases by the extensive generalization of abstract design schemas, which function as both a representation schema storing generalized knowledge and a memory organization facility indexing specific cases and aiding in retrieval. Unlike most current machine learning algorithms, it aims at multiple targets of prediction, feature reminding, case retrieval and new case generation in a single system