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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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References

REFERENCES

Alagappan, V., & Kozaczynski, W. (1991). The evolution of very large information systems. In Automating Software Design, (Lowry, M.R. and McCartney, R.D., Eds.), 3–24. MIT Press, Cambridge, MA.Google Scholar
Barstovv, D. (1984). A perspective on automatic programming. AI Magazine 5(1), 5–27.Google Scholar
Baxter, I. (1992). Design rationale for transformationally constructed software. Proc. AAAI Workshop on Design Rationale Capture and Use, 22–27.Google Scholar
Bhatta, S., Goel, A., & Prabhaker, S. (1994). Innovation in analogical design: A model based approach. In Artificial Intelligence in Design ’94, (Gero, J.S. and Sudweeks, F., Eds.), 57–75. Kluwer Publishers, London.CrossRefGoogle Scholar
Britt, B.D. (1995). Reconstructive derivational analogy. Ph.D. Dissertation, Washington State University.Google Scholar
Britt, B.D., & Glagowski, T.G. (1994). Explanation of designs through heuristic reasoning. In Intelligent Systems, (Yfantis, E.A., Ed.), Vol. 1, pp. 39–44. Kluwer, Amsterdam.Google Scholar
Canfora, G., Cimitile, A., & Carlini, U. (1992). A logic-based approach to reverse engineering tools production. IEEE Transactions on Software Eng.. 18(12), 1053–1063.CrossRefGoogle Scholar
Carbonell, J.G. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In Machine Learning: An Artificial Intelligence Approach 2 (pp. 371–392). Morgan Kaufman, San Mateo, CA.Google Scholar
Chandrasekaran, B. (1990). Design problem solving: A task analysis. AI Magazine 11(4), 59–71.Google Scholar
Coyne, R.D., Rosenman, M.A., Radford, A.D., Balechandran, M., & Gero, J.S. (1990). Knowledge-based design systems. Addison-Wesley, Reading, MS.Google Scholar
Donaldson, I., & MacCallum, K. (1994). The role of computational prototypes in conceptual models for engineering design. In Artificial Intelligence and Design '94, (Gero, J.S., and Sudweeks, F., Eds.), pp. 1–20. Kluwer, London.Google Scholar
Gero, J.S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Glagowski, T., Manwaring, M.L., Somanchi, S.V., & Sy, L.Y. (1991). Automated analog circuit design environment. Proc. Northcon 91, pp. 124–129.Google Scholar
Goel, A. (1991). A model based approach to case adaptation. Proc. 13th Annual Conf. Cognitive Science Society. Chicago, IL, pp. 143–148.Google Scholar
Huhns, M.N., & Acosta, R.D. (1988). ARGO: A system for design by analogy. IEEE Expert 3, 53–68.CrossRefGoogle Scholar
Kant, E. (1985). Understanding and automating algorithm design. IEEE Transactions on Software Engineering 11(11), 1361–1374.CrossRefGoogle Scholar
Langrana, N., Mitchell, T., & Ramachandran, N. (1986). Progress toward a knowledge-based aid for mechanical design. Technical Memo CA1P-TM-002. Center for Computer Aids for Industrial Productivity, Rutgers University.Google Scholar
Liu, J., & Brown, D.C. (1994). Generating design decomposition knowledge for parametric design problems. In Artificial Intelligence and Design '94, (Gero, J.S. and Sudweeks, F., Eds.), pp. 661–678. Kluwer, London.CrossRefGoogle Scholar
Lubars, M.D. (1991). The ROSE-2 strategies for supporting high-level software design reuse. In Automating Software Design, (Lowry, M.R. and McCartney, R.D., Eds.), pp. 93–120. AAAI Press, Menlo Park, CA.Google Scholar
Maher, M.L. (1990). Process models for design synthesis. AI Magazine 11(4), 49–58.Google Scholar
Mitchell, T.M., Mahadevan, S., & Steinberg, L. (1985). LEAP: A learning apprentice for VLSI design. Proc. Ninth Int. Joint Conf. Artif. Intel!. (1JCAI), 573–580.Google Scholar
Mostow, J. (1989). Design by derivational analogy: Issues in the automated replay of design plans. Artif. Intell. 40(1), 119–184.CrossRefGoogle Scholar
Mostow, J., & Barley, M. (1987). Automated reuse of design plans. Proc. 1987 Int. Conf. Eng. Design, 632–647.Google Scholar
Mostow, J., Barley, M., & Weinrich, T. (1989). Automated reuse of design plans. Artif. Intell. Eng. 4(4), 181–196.CrossRefGoogle Scholar
Sacerdoti, E. (1974). Planning in a hierarchy of abstraction spaces. Artif. Intell. 5, 115–135.CrossRefGoogle Scholar
Sriram, D. (1988). Constraint-based design: An application in structural design. In Artificial Intelligence Approaches to Engineering Design, (Tong, C. and Sriram, D., Eds.), Addison-Wesley, New York.Google Scholar
Stefik, M. (1981a). Planning with constraints (MOLGEN: Part 1). Artif. Intell. 16(2), 111–140.CrossRefGoogle Scholar
Stefik, M. (1981b). Planning with constraints (MOLGEN: Part 2). Artif. Intell. 16(2), 141–169.CrossRefGoogle Scholar
Steinberg, L., & Mitchell, T. (1985). The redesign system: A knowledge based approach to VLSI CAD. IEEE Design and Test 2(1), 45–54.CrossRefGoogle Scholar