Learning Prototype-Selection Rules for Case-Based Iterative Design

Mark Schwabacher
Haym Hirsh
Thomas Ellman

Department of Computer Science
Rutgers University
New Brunswick, NJ 08903

CAP-TR-17

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.

Appears in the Proceedings of the Tenth Conference on Artificial Intelligence  
for Applications, San Antonio, Texas, March, 1994