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Reconstructive derivational analogy: A machine learning approach to automating redesign

Published online by Cambridge University Press:  27 February 2009

B.D. Britt  and
T. Glagowski
B.D. Britt
Affiliation:
Computer Science Department, Eastern Washington University, Spokane, WA 99004-2495, U.S.A.
T. Glagowski
Affiliation:
Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752, U.S.A.
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Abstract

This paper describes current research toward automating the redesign process. In redesign, a working design is altered to meet new problem specifications. This process is complicated by interactions between different parts of the design, and many researchers have addressed these issues. An overview is given of a large design tool under development, the Circuit Designer's Apprentice. This tool integrates various techniques for reengineering existing circuits so that they meet new circuit requirements. The primary focus of the paper is one particular technique being used to reengineer circuits when they cannot be transformed to meet the new problem requirements. In these cases, a design plan is automatically generated for the circuit, and then replayed to solve all or part of the new problem. This technique is based upon the derivational analogy approach to design reuse. Derivational Analogy is a machine learning algorithm in which a design plan is saved at the time of design so that it can be replayed on a new design problem. Because design plans were not saved for the circuits available to the Circuit Designer's Apprentice, an algorithm was developed that automatically reconstructs a design plan for any circuit. This algorithm, Reconstructive Derivational Analogy, is described in detail, including a quantitative analysis of the implementation of this algorithm.

Keywords

Derivational AnalogyDesign ReuseDesign HistoryKnowledge-based Circuit Design
Type
Articles
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 115 - 126
Copyright
Copyright © Cambridge University Press 1996

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