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Design as Building and Reusing Artifact Theories: Understanding and Supporting Growth of Design Knowledge

  • Jayachandra M. Reddy
  • Susan Finger
  • Suresh Konda
  • Eswaran Subrahmanian
Conference paper

Abstract

As artifacts are designed, knowledge is accumulated gradually and — as this knowledge is organized and reused — designs and design processes are continually refined. An understanding of the nature and growth of design knowledge and its reuse is essential for implementing better design systems and effective design practices. To develop such an understanding, we introduce artifact theory as an interdisciplinary theory about an artifact that is essential for designing that artifact. This theory encapsulates various types of synthetic, analytic and process knowledge and reconciles many disciplinary theories in the context of the artifact. We argue that it is necessarily a contextual theory and hence is ephemeral. While highly mature and well understood design domains may have complete artifact theories, in most domains artifact theories evolve during design. That is, designers not only produce a manufacturable description of the artifact, but also produce the corresponding artifact theory. We observe that this involves both adaptation and reuse of elements of existing artifact theories as well as development of new elements. Hence, we propose the view of design as building and reuse of artifact theories as the basis for understanding design and for developing design environments. We describe artifact theory in terms of several disparate views of design and bring them together leading to a unifying view. We discuss the implications of the view for computational design environments and outline our current research efforts in advancing and supporting this view.

Keywords

Scientific Theory Turing Machine Technical System Knowledge Building Design Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Suppe F 1977 The Structure of Scientific Theories. University of Illinois Press, Urbana ILGoogle Scholar
  2. 2.
    Mehlberg H 1962 The theoretical and emperical aspects of science. In: Nagel E, Supes P, Tarskied A (eds) Proceedings of the 1960 Congress on Logic Methodology and Philosophy of Science. Stanford University Press, Palo Alto CA, pp 275–284Google Scholar
  3. 3.
    Addis W 1990 Structural Engineering: The Nature of Theory and Design. Ellis Horwood Limited, West Sussex EnglandGoogle Scholar
  4. 4.
    Reddy J M, Chan B, Finger S 1996 Patterns in design discourse: a case study. In: Knowledge Intensive CAD, Volume 1. Chapman & Hall, London, pp 265–283Google Scholar
  5. 5.
    Cutkosky M, Tenenbaum J, Glucksman J 1996 Madefast: an exercise in collaborative engineering over the internet. Communications of the ACM 39(9):78–87CrossRefGoogle Scholar
  6. 6.
    Siewiorek D P, Smailagic A, Lee J CY, Adl-Tabatabai A R 1994 Interdisciplinary concurrent design methodology as applied to the navigator wearable computer system. Journal of Computer and Software Engineering 2(3): 259–292Google Scholar
  7. 7.
    Finger S, Stivoric J, Amon C. et al. 1996 Reflections on a concurrent design methodology: A case study in wearable computer design. Computer-Aided Design 28(5):393–404CrossRefGoogle Scholar
  8. 8.
    Finger S, Gardner E, Subrahmanian E 1993 Design support systems for concurrent engineering: A case study in large power transformer design. In: Proceedings of The International Conference on Engineering Design ICED′93. The Hauge, pp 1433–1440Google Scholar
  9. 9.
    McMahon C 1994 Observations on modes of incremental change in design. Journal of Engineering Design 5(3): 195–209CrossRefGoogle Scholar
  10. 10.
    Duffy A H B, Kerr S M 1993 Customised perspectives of past designs from automated group rationalisations. Artificial Intelligence in Engineering 8:183–200CrossRefGoogle Scholar
  11. 11.
    Duffy A H B, Duffy S M 1996 Learning for design reuse. Artificial Intelligence for Engineering Design Analysis and Manufacturing 10:139–142CrossRefGoogle Scholar
  12. 12.
    Vincenti W 1990 What Engineers Know and How They Know It. John Hopkins University Press, Baltimore MDGoogle Scholar
  13. 13.
    Petroski H 1985 To Engineer is Human: The Role of Failure in Successful Design. St Martin’s Press, New YorkGoogle Scholar
  14. 14.
    Meyer S A 1995 Description of the structural design of tall buildings through the grammar paradigm. PhD thesis, Carnegie Mellon UniversityGoogle Scholar
  15. 15.
    Yoshikawa H 1981 General design theory and a CAD system. In: Man-Machine Communication in CAD/CAM. North Holland, Amsterdam, pp 35–58Google Scholar
  16. 16.
    Tomiyama T, Yoshikawa H 1986 Extended General Design Theory. Tech Report CS-R8604 Centrum voor Wiskunde en Informatica, AmsterdamGoogle Scholar
  17. 17.
    Tomiyama T, Yoshikawa H 1985 Extended general design theory. In Design Theory in Computer-Aided Design North Holland, Amsterdam, pp 95–130Google Scholar
  18. 18.
    Reich Y A 1995 Critical review of general design theory. Research in Engineering Design 7(1):1–1CrossRefGoogle Scholar
  19. 19.
    Fitzhorn P A 1994 Engineering design is a computable function. Artificial Intelligence in Engineering Design and Manufacturing 8(1):35–44CrossRefGoogle Scholar
  20. 20.
    Hubka V, Eder W E 1988 Theory of Technical Systems: A Total Concept Theory for Engineering Design. Springer-Verlag, New YorkGoogle Scholar
  21. 21.
    Monarch I A, Konda S L, Levy S N, Reich Y, Subrahmanian E, Ulrich C 1993 Shared memory in design: theory and practice. In: Social Science Research Technical Systems and Cooperative Work. Paris, pp 227–241Google Scholar
  22. 22.
    Yoshikawa H 1992 Proposal for artifactual engineering: aims to make science and technology self-conclusive. In: Illume A Tepco Semiannual Review Tokyo Electric Power Co Inc., Tokyo, pp 41–56Google Scholar
  23. 23.
    Suh N P 1988 The Principles of Design. Oxford University Press, OxfordGoogle Scholar
  24. 24.
    Naur P 1985 Programming as theory building. Microprocessing and Microprogramming 15:253–261CrossRefGoogle Scholar
  25. 25.
    Bucciarelli L L 1988 An ethnographic perspective on engineering design. Design Studies 9(3):159–168CrossRefGoogle Scholar
  26. 26.
    Rittel H, Webber M 1973 Dilemmas in a general theory of planning. Policy Sciences 4:155–169CrossRefGoogle Scholar
  27. 27.
    Hubka V (ed) 1991 Proceedings of ICED′91 International Conference on Engineering Design. WDK, ZurichGoogle Scholar
  28. 28.
    Reddy J 1996 Building and reuse of artifact theories: A view of design and its implications for computational environments. PhD thesis, Carnegie Mellon UniversityGoogle Scholar
  29. 29.
    Hong J, Toye G, Leifer L 1995 PENS: personal electronic notebook with sharing. In: Fourth IEEE Workshop on Enabling Technologies. Berkeley Springs West VirginiaGoogle Scholar
  30. 30.
    Finger S, Konda S, Subrahmanian E 1995 Concurrent design happens at the interfaces. Artificial Intelligence for Engineering Design Analysis and Manufacturing 9:89–99CrossRefGoogle Scholar
  31. 31.
    Cutkosky M R, Engelmore R S, Fikes R E et al. 1993 PACT, an experiment in integrating concurrent engineering systems. Computer 26(1):28–37CrossRefGoogle Scholar
  32. 32.
    Olsen G, Cutkosky M, Tenenbaum J M, Gruber T 1994 Collaborative engineering based on knowledge sharing agreements. Proceedings of the ASME Database Symposium. Minneapolis MN pp 11–14Google Scholar
  33. 33.
    Levy S, Subrahmanian E, Konda S, Coyne R, Westerberg A, Reich Y 1993 An Overview of n-dim Environment. EDRC Technical Report 05-65-93 Carnegie Mellon UniversityGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1998

Authors and Affiliations

  • Jayachandra M. Reddy
    • 1
  • Susan Finger
    • 2
  • Suresh Konda
    • 2
  • Eswaran Subrahmanian
    • 2
  1. 1.Rockwell Science CenterPalo AltoUSA
  2. 2.Engineering Design Research CenterCarnegie Mellon UniversityPittsburghUSA

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