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A foundation for machine learning in design

Sim, Siang Kok and Duffy, Alex H.B. (1998) A foundation for machine learning in design. AI EDAM - Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12 (2). pp. 193-209. ISSN 0890-0604

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    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.

    Item type: Article
    ID code: 6427
    Keywords: design knowledge, design process knowledge, design Reuse, knowledge transformation, learning design, machine learning, artificial intelligence, Electronic computers. Computer science, Engineering (General). Civil engineering (General), Engineering design, Artificial Intelligence, Industrial and Manufacturing Engineering
    Subjects: Science > Mathematics > Electronic computers. Computer science
    Technology > Engineering (General). Civil engineering (General)
    Technology > Engineering (General). Civil engineering (General) > Engineering design
    Department: Faculty of Engineering > Design, Manufacture and Engineering Management
    Related URLs:
      Depositing user: Miss Caroline Torres
      Date Deposited: 03 Jul 2008
      Last modified: 02 Jul 2014 16:41
      URI: http://strathprints.strath.ac.uk/id/eprint/6427

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