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A foundation for machine learning in design

Published online by Cambridge University Press:  01 April 1998

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

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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.

Keywords

Design KnowledgeDesign Process KnowledgeDesign ReuseKnowledge Transformation/ChangeLearning in DesignMachine Learning Techniques
Type
Research Article
Information
AI EDAM , Volume 12 , Issue 2 , April 1998 , pp. 193 - 209
Copyright
© 1998 Cambridge University Press