A foundation for machine learning in design

Siang Kok Sim, Alex H.B. Duffy

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
190 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)193-209
Number of pages17
JournalAI EDAM - Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Issue number2
Publication statusPublished - 1998


  • design knowledge
  • design process knowledge
  • design Reuse
  • knowledge transformation
  • learning design
  • machine learning
  • artificial intelligence


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