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Acquiring design knowledge through design decision justification

Published online by Cambridge University Press:  27 February 2009

Ana Cristina Bicharra Garcia  and
H. Craig Howard
Ana Cristina Bicharra Garcia
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305, U.S.A.
H. Craig Howard
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305, U.S.A.
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Abstract

Currently design documentation rarely records the designer's decision process or the reasons behind those decisions. This paper describes an effort to improve design documentation by having the computer act as an intelligent apprentice to the designer to capture the rationale during the design process. The apprentice learns about the features that make a specific case different from the standard. Whenever the designer proposes a design action that differs from the apprentice's expectations, the interface will ask for the designer for justifications to explain the differences. Later queries for design rationale are answered using a combination of the apprentice's domain knowledge and the designer-supplied justifications. The apprentice model is being implemented in a prototype system called ADD (Augmenting Design Documentation). The initial focus of the work is on HVAC (Heating, Ventilation, and Air Conditioning) design. Our starting point for implementing the apprentice model is observing how people develop HVAC system designs and then explain those designs.

Information

Type
Research Article
Information
AI EDAM , Volume 6 , Issue 1 , February 1992 , pp. 59 - 71
Copyright
Copyright © Cambridge University Press 1992

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References

Baudin, C., Sivard, C and Zweben, M 1990. A model-based approach to design rationale conservation. IJCAI-Workshop on Model-Based Reasoning, 88–90. Detroit.Google Scholar
Conklin, J. and Begeman, M. L. 1988. gIBIS: A hypertext tool for exploratory policy discussion. Computer-Supported Cooperative Work, 140–152. ACM.Google Scholar
Fischer, G., Lemke, A. and McCall, R. 1992. Making argumentation serve design. Human-Computer Interaction Journal in press.Google Scholar
Fischer, G. and McCall, R. 1989. JANUS: Integrating hypertext with a knowledge-based design environment. Hypertext '89 Papers, 105–117.Google Scholar
Ganeshan, R., Finger, S and Garrett, J. 1991. Representing and reasoning with design intent. Artificial Intelligence in Design ’91, pp. 737–755, Edinburgh, U.K.Google Scholar
Garcia, A. C. B. and Howard, H. C. 1991. Building a model for augmented design documentation. Artificial Intelligence in Design –91, pp. 723–736, Edinburgh, U.K.Google Scholar
Genesereth, M. R., Kellogg, C. and Rosenberg, S. 1989. The Designworld project. Report Logic-89–3, Stanford University, CA.Google Scholar
Gruber, T. R. and Russell, D. M. 1990. Design knowledge and design rationale: A framework for representation, capture and use. Technical report KSL 90–45, Knowledge Systems Laboratory, Stanford University, CA.Google Scholar
Guindon, R. 1990 a. Designing the design process: Exploiting opportunistic thoughts. Human-Computer Interaction 5, 305–344.CrossRefGoogle Scholar
Guindon, R. 1990 b. Knowledge exploited by experts during software system design. International Journal Man-Machine Studies 33, 279–304.CrossRefGoogle Scholar
Howard, H. C., Wang, J., Daube, F. and Rafiq, T. 1989. Applying design-dependent knowledge in structural engineering design. Artificial Intelligence in Engineering, Design and Manufacturing 3 (2), 111–123.Google Scholar
Kunz, W. and Rittel, H. W. J. 1970. Issue as elements of information systems. Technical Report. Center for Planning and Development Research, University of California, Berkeley.Google Scholar
Lakin, F., Wambaugh, J., Leifer, L., Cannon, D. and Sivard, C. 1989. The electronic design notebook: Performing medium and processing medium. Visual Computer: International Journal of Computer Graphics 5, 214–226.CrossRefGoogle Scholar
Lee, J. 1990. SIBYL: A qualitative design management system. In Artificial Intelligence at MIT Expanding Frontiers Vol 1, Winston, P. and Shellard, S. eds, pp. 104–133, Cambridge, MA: M.I.T. Press.Google Scholar
McCall, R. 1987. PHIBIS: Procedurally hierarchical issue-based information systems. Proceedings of the Conference on Architecture at the International Congress on Planning and Design Theory, American Society of Mechanical Engineers, New York, 1987.Google Scholar
Marcus, S., 1989. Understanding decision ordering from a piecemeal collection of knowledge. Knowledge Acquisition 1, 279–298.CrossRefGoogle Scholar
Simon, H. A. 1981. The Sciences of the Artificial. Second Edition. Cambridge, MA: M.I.T. Press.Google Scholar
Sussman, G., Holloway, J. and Knight, T. F. 1979. Computer aided evolutionary design for digital integrated systems. M.I.T. Artificial Intelligence Laboratory Memo 526, Cambridge, MA.Google Scholar
Rafiq, T. 1990. Project-specific knowledge for facility engineering. Unpublished PhD thesis proposal. Department of Civil Engineering, Stanford University, CA.Google Scholar