New roles for machine learning in design

Yoram Reich, Suresh L. Konda, Sean N. Levy, Ira A. Monarch, Eswaran Subrahmanian

Research output: Contribution to journalArticlepeer-review

Abstract

Research on machine learning in design has concentrated on the use and development of techniques that can solve simple well-defined problems. Invariably, this effort, while important at the early stages of the development of the field, cannot scale up to address real design problems since all existing techniques are based on simplifying assumptions that do not hold for real design. In particular, they do not address the dependence on context and multiple, often conflicting, interests that are constitutive of design. This paper analyzes the present situation and criticises a number of prevailing views. Subsequently, the paper offers an alternative approach whose goal is to advance the use of machine learning in design practice. The approach is partially integrated into a modeling system called n-dim. The use of machine learning in n-dim is presented and open research issues are outlined.

Original languageEnglish
Pages (from-to)165-181
Number of pages17
JournalArtificial Intelligence in Engineering
Volume8
Issue number3
DOIs
StatePublished - 1993
Externally publishedYes

Keywords

  • computational models
  • design
  • design practice
  • flexible modeling
  • machine learning
  • multistrategy learning
  • natural language processing
  • shared memory

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