Using modeling knowledge to guide design space search

  • Authors:

  • Andrew Gelsey;Mark Schwabacher;Don Smith

  • Affiliations:

  • Computer Science Department, Rutgers University, New Brunswick, NJ 08903, USA;Computer Science Department, Rutgers University, New Brunswick, NJ 08903, USA;Computer Science Department, Rutgers University, New Brunswick, NJ 08903, USA

  • Venue:
  • Artificial Intelligence
  • Year:
  • 1998

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Abstract

Automated search of a space of candidate designs is an attractive way to improve the traditional engineering design process. To make this approach work, however, an automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and an effective way to use this knowledge to influence the search process. We argue that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumption is violated. The search is then guided by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. A key result of our work is a successful demonstration of the application of AI techniques to an important engineering problem. In an empirical study of parametric conceptual aircraft design, we observed a cost improvement of two orders of magnitude. The principal contribution of our work is a new design optimization methodology which makes explicit the interaction between models of artifacts, and validity models of artifact models.