Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-26T23:53:10.118Z Has data issue: false hasContentIssue false
  • English
  • Français

Research methodology for AI and design

Published online by Cambridge University Press:  27 February 2009

Louis Steinberg
Louis Steinberg
Affiliation:
Department of Computer Science, Hill Center, Busch Campus, Rutgers University, Piscataway, NJ08855-1089.
Get access Rights & Permissions [Opens in a new window]

Abstract

Perspectives on research methodology in the field of “AI and design” are discussed. This perspective is based on a view of a “science of design” focusing on methods of design and on characteristics of design tasks that affect what methods are relevant for a given task. Two methodological issues are discussed: the need to try applying a design method on multiple tasks and domains, and the need to work with collaborators who are experts in the task domain of each research system you build.

Keywords

Science of DesignResearch MethodologyDomain-independence
Type
Articles
Information
AI EDAM , Volume 8 , Issue 4 , Fall 1994 , pp. 283 - 287
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Lenat, D.B., & Brown, J.S. (1984). Why AM and EURISKO appear to work. Artificial Intelligence 23(3), 269–294.CrossRefGoogle Scholar
Steinberg, L. (1987). Design as refinement plus constraint propagation: The VEXED experience. In Proc. Sixth Nat. Conf. Artificial Intelligence, Vol. 2, Seattle, Washington, 830–835; Rutgers University AI/VLSI Project Working Paper No. 51.Google Scholar
Steinberg, L. (1989). Dimensions for categorizing design tasks. In AAAI Spring 1989 Symp. on AI and Manufacturing, available as Rutgers AI/Design Project Working Paper Number 127.Google Scholar
Steinberg, L. (1992). Design as top-down refinement plus constraint propagation. In Artificial Intelligence In Engineering Design, Vol. 1, (Tong, C., & Sriram, D., Eds.), Ch. 8, pp. 251–272. Academic Press, New York.CrossRefGoogle Scholar
Steinberg, L., & Langrana, N. (1994). EVEXED and MEET for mechanical design: Testing structural decomposition and constraint propagation. Forthcoming.Google Scholar