Learning prototype-selection rules for case-based iterative design

The first step for most case-based design systems is to select an initial prototype from a database of previous designs. The retrieved prototype is then modified to tailor it to the given goals. For any particular design goal the selection of a starting point for the design process can have a dramatic effect both on the quality of the eventual design and on the overall design time. We present a technique for automatically constructing effective prototype-selection rules. Our technique applies a standard inductive-learning algorithm, C4.5, to a set of training data describing which particular prototype would have been the best choice for each goal encountered in a previous design session. We have tested our technique in, the domain of racing-yacht-hull design, comparing our inductively learned selection rules to several competing prototype-selection methods. Our results show that the inductive prototype-selection method leads to better final designs when the design process is guided by a noisy evaluation function, and that the inductively learned rules will often be more efficient than competing methods