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A Method for Inferring Design Rules With Explicit Bounds of Applicability

[+] Author Affiliations
Kevin Rawson, Thomas F. Stahovich

University of California at Riverside, Riverside, CA

Paper No. DETC2005-85295, pp. 33-42; 10 pages
doi:10.1115/DETC2005-85295
From:
  • ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Design Automation Conference, Parts A and B
  • Long Beach, California, USA, September 24–28, 2005
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4739-X | eISBN: 0-7918-3766-1
  • Copyright © 2005 by ASME

abstract

We present a two-step technique for learning reusable design procedures from observations of a designer in action. This technique is intended for the domain of parametric design problems in which the designer iteratively adjusts the parameters of a design so as to satisfy the design requirements. In the first step of the two-step learning process, decision tree learning is used to infer rules that predict which design parameter the designer will change for any particular state of an evolving design. In the second step, decision tree learning is again used, but this time to learn explicit termination conditions for the rules learned in the first step. The termination conditions are used to predict how large of a parameter change should be made when a rule is applied. The learned rules, and termination conditions, can be used to automatically solve new design problems with a minimum of human intervention. Initial experiments with this technique suggest that it is considerably more efficient than the previous technique which was incapable of learning explicit rule termination conditions. In particular, the rule termination conditions allow a program to automatically solve new design problems with far fewer iterations than required with the previous approach.

Copyright © 2005 by ASME
Topics: Design

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