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Function–behavior–structure paths and their role in analogy-based design

Published online by Cambridge University Press:  27 February 2009

Lena Qian  and
John S. Gero
Lena Qian
Affiliation:
Canon Information Systems Research Australia, 1 Thomas Holt Drive, North Ryde, NSW 2113, Australia
John S. Gero
Affiliation:
Key Centre of Design Computing, Department of Architectural and Design Science, University of Sydney, NSW 2006, Australia
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Abstract

In many creative design processes, cross-domain knowledge is required to inspire the new design result. Thus, in knowledge-based design, how we represent the cross-domain knowledge becomes a key issue. In this paper, we present a formalism for design knowledge representation. By analyzing function representation in different design domains, from graphic design and industrial design to architectural and engineering device designs, we find that although the focus of each kind of design is different, the function representation can be generalized into a small number of categories. This formalism can be used in an explorative model of design by analogy, where designs from different design domains are sources to help produce a new design.

Keywords

Creative DesignDesign by AnalogyFunction-Behavior-Structure

Information

Type
Articles
Information
AI EDAM , Volume 10 , Issue 4: Special Issue: Representing functionality in design , September 1996 , pp. 289 - 312
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Bhansali, S., & Harandi, M. (1992). Synthesizing UNIX programming using derivational analogy. No. KSL 92–02. Knowledge Systems Laboratory, Stanford University, Stanford.Google Scholar
Bobrow, D.G. (Ed.) (1984). Qualitative reasoning about physical systems. Elsevier, North-Holland, Amsterdam.CrossRefGoogle Scholar
Carbonell, J.G. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In Machine Learning II: An Artificial Intelligence Approach, (Michalski, R.S., Carbonell, J.G., and Mitchell, T.M., Eds.), pp. 371–392. Morgan Kaufmann, Los Altos, California.Google Scholar
DeKleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artif. Intell. 24, 7–83.CrossRefGoogle Scholar
Falkenhainer, B., Forbus, K.D., & Gentner, D. (1989/1990). The structuremapping engine: Algorithm and examples. Artif. Intell. 41, 1–63.CrossRefGoogle Scholar
Forbus, D.K., & Gentner, D. (1990). Causal reasoning about quantities. In Readings in Qualitative Reasoning About Physical Systems, (Weld, D.S., and DeKleer, J., Eds.), pp. 666–677. Morgan Kaufmann, Los Altos, California.CrossRefGoogle Scholar
Gero, J.S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Gero, J.S. (1994). Towards a model of exploration in computer-aided design. In Formal Design Methods for CAD, (Gero, J.S., and Tyugu, E., Eds.), pp. 315–336. North-Holland, Amsterdam.Google Scholar
Iwasaki, Y., & Chandrasekaran, B. (1992). Design verification through function- and behaviour-oriented representations: Bridging the gap between function and behaviour. In Artificial Intelligence in Design '92, (Gero, J.S., Ed.), pp. 597–616. Kluwer, Dordrecht.Google Scholar
Kedar-Cabelli, S.T. (1988). Toward a computational model of purposedirected analogy. In Analogica, (Prieditis, A., Ed.), pp. 89–107. Morgan Kaufmann, Pitman, London.Google Scholar
Kolodner, J. (1991). Improving human decision making through casebased decision making. AI Magazine 12, 52–68.Google Scholar
Maher, M.L., Balachandran, B., & Zhang, C.M. (1995). Case-based reasoning in design. Lawrence Erlbaum, Hillsdale, NJ.Google Scholar
Maher, M.L., Zhao, F., & Gero, J.S. (1989). Creativity in humans and computers. In Knowledge-Based Systems in Architecture, (Gero, J.S., and Oksala, T., Eds.), pp. 129–141. Acta Polytechnica Scaondinavica, Helsinki.Google Scholar
Qian, L. & Gero, J.S. (1993). Design part classification by goal achievement. In Reasoning About Function, (Kumar, A.N., Ed.), pp. 121–125. Workshop Preprints, AAAI-93, Washington, DC.Google Scholar
Qian, L. (1995). Creative Design by Analogy, Ph.D. Thesis, Department of Architectural and Design Science, University of Sydney, Australia.Google Scholar
Rosenman, M.A., Gero, J.S., & Oxman, R.E. (1991). What's in a case: The use of case based, knowledge based and databases in design. In CAAD Futures '91, (Schmitt, G.N., Ed.), pp. 263–277. ETH, Zurich.Google Scholar
Sembugamoorthy, V., & Chandrasekaran, B. (1986). Functional representation of devices and compilation of diagnostic problem-solving systems. In Experience, Memory and Reasoning, (Kolodner, J., and Riesbeck, C., Eds.), pp. 47–73. Erlbaum, Hillsdale, NJ.Google Scholar
Sycara, K., & Navinchandra, D. (1991). Index transformation techniques for facilitating creative use of multiple cases. In IJAI-91 Workshop on AI in Design, (Gero, J.S., Ed.), pp. 15–20. University of Sydney, Australia.Google Scholar
Tham, K.W., Lee, H.S., & Gero, J.S. (1990). Building envelope design using design prototypes. ASHRAE Trans. 96(2), 508–520.Google Scholar
Wirth, R., & O'Rorke, P. (1993). Representing and reasoning about function for failure modes and effects analysis. In Reasoning about Function, pp. 188–194. Workshop Preprints, AAAI-93, Washington, DC.Google Scholar
Zhao, F., & Maher, M. (1992). Using network-based prototypes to support creative design by analogy and mutation. In Artificial Intelligence in Design '92, (Gero, J.S., Ed.), pp. 773–793. Kluwer, Dordrecht.Google Scholar