AI EDAM


Special Issue: Machine Learning in DesignGuest EditorsAlex H.B. DuffyDavid C. BrownAshok K. Goel

A foundation for machine learning in design


SIANG KOK SIM a1 and ALEX H.B. DUFFY a2c1
a1 School of Mechanical and Production Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Republic of Singapore
a2 CAD Centre, University of Strathclyde, 75 Montrose Street, Glasgow G11XJ, Scotland, UK

Abstract

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, “What types of knowledge can be learnt?”, “How does learning occur?”, and “When does learning occur?”. Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD.

(Received June 27 1997)
(Revised October 17 1997)
(Accepted November 10 1997)


Key Words: Design Knowledge; Design Process Knowledge; Design Reuse; Knowledge Transformation/Change; Learning in Design; Machine Learning Techniques.

Correspondence:
c1 Reprint requests to: Dr. A.H.B. Duffy, CAD Centre, University of Strathclyde, 75 Montrose Street, Glasgow G11XJ, Scotland, UK. Tel: (+44)141-548-3134; Fax: (+44)141-552-3148; E-mail: alex@cad.strath.ac.uk.


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