Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-06-03T21:46:26.879Z Has data issue: false hasContentIssue false
  • English
  • Français

Engineering vision: Considerations in a meaningful approach to conceptual design

Published online by Cambridge University Press:  27 February 2009

Martin R. Ramirez
Martin R. Ramirez
Affiliation:
The Johns Hopkins University, Baltimore, MD 21218, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

A method is proposed for creative innovative design that is in concordance with the act of knowledge integration in learning; creative innovative design is defined as a guided (creative) process for arriving at an artifact that is socially valuable (practical and needed) and original (innovative). Within the context of models of reasoning, the process of design is interpreted and analyzed with a goal of extracting the stages at which it can be consciously improved by mindful control. A language is proposed for team-oriented intra- or interdisciplinary collaborative, as well as individual design that facilitates communication, mutual understanding, and makes explicit alternative nonverbal, nonquantitative thinking processes that otherwise may remain latent. The role of motivation in innovation is briefly discussed, as well as the role of artifact valuation in a societal context. Although not central to the present discussion, computer models of design are presented as an instance of design practice captured for computer-based design support. A brief discussion highlights the application and implications of the proposed method to education and research.

Keywords

InnovationCreativitySocial ContextDesign TheorySemiotic Theory
Type
Articles
Information
AI EDAM , Volume 10 , Issue 3 , June 1996 , pp. 199 - 214
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Albano, L.D. & Suh, N.P. (1994). Axiomatic design and concurrent engineering. Computer Aided Design 26 (7), 499–504.CrossRefGoogle Scholar
Amabile, T.M. (1983). The social psychology of creativity. Springer-Verlag, New York.CrossRefGoogle Scholar
Barker, J.A. (1989). Discovering the future: The business of paradigms. Charthouse Learning Corp.Google Scholar
Barrows, H.S., & Tamblyn, R.M. (1982). Problem-based learning: An approach to medical education. Springer-Verlag, New York.Google Scholar
Beardsley, M.C. (1965). On the creation of art.J. Aesthetics and Art Criticism 23, 291–301.CrossRefGoogle Scholar
Boden, M.A. (1990). The creative mind: Myths & mechanisms. Basic Books, New York.Google Scholar
Booch, G. (1986). Object-oriented development. IEEE Trans. Software Eng. 12 (2), 211–221.CrossRefGoogle Scholar
Bower, G.H., Black, J.B., & Turner, T.J. (1979). Scripts in text comprehension and memory. Cogn. Psychol. 11, 177–220.CrossRefGoogle Scholar
Bruner, J. (1986). Actual minds, possible worlds. Harvard University Press, Cambridge.CrossRefGoogle Scholar
Cagan, J., & Agogino, A.M. (1987). Innovative design of mechanical structures from first principles. AI EDAM 1 (3).Google Scholar
Carbonell, J.G. (1978). Politics: Automated ideological reasoning. Cogn. Sci. 2 (1), 27–51.CrossRefGoogle Scholar
Carbonell, J.G. (1979). Subjective understanding: Computer models of belief systems. Ph.D. dissertation (Report No. 150), Yale University, New Haven, CT.Google Scholar
Carlson, S.E. (1993). Component selection optimization using gentic algorithms. Ph.D. dissertation, Georgia Institute of Technology, Atlanta, GA.Google Scholar
Cook, H.E. (1991). On competitive manufacturing enterprises II: S-model paradigms. Manufact. Rev. 4 (2), 106–114.Google Scholar
Cook, H.E., & DeVor, R.E. (1991). On competitive manufacturing enterprises I: The S-model and the theory of quality. Manufact. Rev. 4 (2), 96–105.Google Scholar
Cullingford, R.E. (1978). Script application: Computer understanding of newspaper stories. Ph.D. Dissertation (Report No. 116), Yale University,New Haven, CT.Google Scholar
de Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artif. Intell. 24, 7–83.CrossRefGoogle Scholar
Drucker, P.F. (1989). The new realistics. Harper and Row, New York.Google Scholar
Dyer, M.G., Flowers, M., & Hodges, J. (1986). EDISON: An engineering design invention system operating naively. In Proc. First Int. Conf. Apptic. AI Eng. Probl. (Sriram, D., & Adey, R.A., Eds.), Springer-Verlag, London.Google Scholar
Ecker, D.W. (1963). The artistic process as qualitative problem solving. J. Aesthetics Art Criticism 21, 283–296.CrossRefGoogle Scholar
Edmondson, A.C. (1987). A Fuller explanation: The synergistic geometry of Buckminster Fuller. Birkhauser, Boston.CrossRefGoogle Scholar
Evangelos, S. (1991). Case-based reasoning. JAI Press, London.Google Scholar
Ferguson, E.S. (1977). The mind's eye: Nonverbal thought in technology. Science 197, 827–836.CrossRefGoogle Scholar
Ferguson, E.S. (1992). Engineering and the mind–s eye. MIT Press, Cambridge.Google Scholar
Feynman, R.P. (1988). What do you care what other people think? Basic Books, New York.Google Scholar
Forbus, K.D. (1984). Qualitative process theory. Artif. Intell. 24, 85–168.CrossRefGoogle Scholar
Ganeshan, R., Finger, S., & Garrett, J. (1991). Representing and reasoning with design intent. In Proc. Artif. Intell. Design '91 (Gero, J.S., Ed.), Butterworth-Heinemann Ltd., Oxford.Google Scholar
Garcia, A.C.B., & Howard, H.C. (1991). Building a model for augmented design documentation. In Proc. Artif. Intell. Design '91 (Gero, J.S., Ed.), Butterworth-Heinemann Ltd. Oxford.Google Scholar
Gardner, H. (1983–1993). Frames of mind: The theory of multiple intelligences. Basic Books, New York.Google Scholar
Gero, J.S., & Maher, M.L. (1993). Modeling creativity and knowledge-based creative design. Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Goldberg, D. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading.Google Scholar
Graeser, A.C., Gordon, S.E., & Sawyer, J.D. (1979). Recognition memory for typical and atypical actions in scripted activities: Tests of a script pointer and tag hypothesis. J. Verbal Learn. Verbal Behav. 1 (8), 319–332.CrossRefGoogle Scholar
Graham, M., & Pruitt, B. (1990). R&D for industry. Cambridge University Press, Cambridge.Google Scholar
Guilford, J.P. (1967). The nature of human intelligence. McGraw-Hill, New York.Google Scholar
Hadamard, J. (1945). The psychology of invention in the mathematical field. Princeton University Press, Princeton.Google Scholar
Hayes, P.J. (1985). The second naive physics manifesto. In Formal Theories of the Commonsense World, (Hobbs, J.. and Moore, B., Eds.), pp. 1–36. Ablex.Google Scholar
Heath, T. (1984). Method in Architecture, John Wiley, Chichester.Google Scholar
Holton, G. (1972). On trying to understand scientific genius. Am. Scholar 41, 95–110.Google Scholar
Holton, G. (1988). Thematic origins of scientific thought, 2nd ed. Harvard University Press, Cambridge.Google Scholar
Howard, W.G. Jr, & Guile, B.R. (1991). Profiting from innovation: Management tools and techniques, part I. Manufact. Rev. 4 (4), 237–245.Google Scholar
Huhns, M.N., & Acosta, R.D. (1987). Argo: An analogical reasoning system for solving design problems (Report No. MCC-AI/CAD-092–87). Austin, TX: Microelectronics and Computer Technology Corporation.Google Scholar
Khatchadourian, H. (1977). The creative process in art. Br. J. Aesthetics 17 (3), 230–241.CrossRefGoogle Scholar
Kolodner, J.L. (1993). Case-based reasoning. Morgan Kaufman, San Mateo.CrossRefGoogle Scholar
Kuhn, T. (1962). The structure of scientific revolutions. University of Chicago Press, Chicago.Google Scholar
Kuipers, B.J. (1986). Qualitative simulation. Artif. Intell. 29, 289–338.CrossRefGoogle Scholar
Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. Basic Books, New York.CrossRefGoogle Scholar
Lenat, D.B. (1982). An artificial intelligence approach to discovery in mathematics as heuristic search. In Knowledge-Based Systems in AI, (Davis, R. and Lenat, D.B., Eds.), McGraw-Hill, New York.Google Scholar
MacKenzie, N. (1965). Dreams and dreaming. Aldus Books, London.Google Scholar
McKim, R.H. (1980). Experiences in visual thinking. PWS Publishing, Boston.Google Scholar
McKim, R.H., & Woolsey, D. (1994). Vizthink: Experiences in visual thinking. PWS Publishing, Boston.Google Scholar
McLaughlin, S. (1993). Emergent value in creative products. In Modeling Creativity and Knowledge-Based Creative Design (J., Gero, Ed.). Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Murdock, G.P. (1956). How culture changes. In Man, Culture, and Society, (Shapiro, H.L., Ed.), pp. 247–260. Oxford University Press, New York.Google Scholar
Murthy, S.S., & Addanki, S. (1987). PROMPT: An innovative design tool. Proc. AAAI.Google Scholar
NAS/NRC (1991). The competitive edge: Research priorities for U.S. manufacturing. National Academy Press, Washington.Google Scholar
Navinchandra, D. (1991). Exploration and innovation in design: Towards a computational model. Springer-Verlag, New York.CrossRefGoogle Scholar
Nelson, K., & Gruendel, J. (1978). From person episode to social script: Two dimensions in the development of event knowledge. Proc. Soc. Res. Child Dev., San Francisco.Google Scholar
Nilsson, N.J. (1980). Principles of artificial intelligence. Tioga Publishing Co.Google Scholar
Perkins, D.N. (1986). Knowledge as design. Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Piaget, J. (1954). The construction of reality in the child. Basic Books, New York.CrossRefGoogle Scholar
Ram, A., Arkin, R.C., Moorman, K., & Clark, R.J. (1992). Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters in autonomous robotic systems. (Report No. GIT-CC-92/57). Atlanta, GA: College of Computing, Georgia Institute of Technology.Google Scholar
Ramirez, M.R. (1991). Computational artificial intelligence: A hybrid computational-heuristic knowledge-based system for setting timesteps in dynamic finite element programs. In Proc. 2nd Int. Conf. Appiic. Artif. Intell. Tech. Civil Struct. Eng., (Topping, B.H.V., Ed.), Oxford, England.Google Scholar
Ramirez, M.R. (1994 a). An integrative theory of instruction. Submitted for publication.Google Scholar
Ramirez, M.R. (1994 b). Qualitative reasoning processes for innovative structural design: A cognitive perspective. Submitted for publication.Google Scholar
Rich, E. (1983). Artificial intelligence. McGraw-Hill, New York.Google Scholar
Robertson, D., Ulrich, K., & Filerman, M. (1991). CAD and cognitive complexity: Beyond the drafting board metaphor. Manufact. Rev. 4(3).Google Scholar
Rumelhart, D. (1980). Schemata: The building blocks of cognition. In Theoretical Issues in Reading Comprehension, (Spiro, R., Bruce, B. and Brewer, W., Eds.), Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Salustri, F.A. & Venter, R.D. (1992). An axiomatic theory of engineering design information. Engineering With Computers 8(4), 197–211.CrossRefGoogle Scholar
Schank, R.C. (1972). Conceptual dependency: A theory of natural language understanding. Cogn. Psychol. 3(4), 552–631.CrossRefGoogle Scholar
Schank, R.C. (1981). Inside computer understanding: Five programs plus miniatures. Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Schank, R.C. (1984). The cognitive computer. Addison-Wesley, Reading.Google Scholar
Schank, R.C. (1986). Explanation patterns: Understanding mechanically and creatively. Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Schank, R.C, & Abelson, R.P. (1977). Scripts, plans, goals, and understanding. Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Schoenfeld, A.H. (1989). Explorations of students mathematical beliefs and behavior. Journal For Research In Mathematics Education 20(4), 338–355.CrossRefGoogle Scholar
Simon, H.A. (1960). Decision-making and administration organization. Bobbs-Merrill, Indianapolis.Google Scholar
Simon, H.A. (1969). The sciences of the artificial. MIT Press, Cambridge.Google Scholar
Smith, E.E., Adams, N., & Schorr, D. (1978). Fact retrieval and the paradox of interference. Cogn. Psychol. 10, 438–464.CrossRefGoogle Scholar
Sriram, D., Locher, R.D., & Cherneff, J. (1989). DICE: An objectoriented programming environment for cooperative engineering design. Technical Report of the Intelligent Systems Laboratory, Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
Sternberg, R.J. (1991). An investment theory of creativity and its development. Hum. Devel. 34(1), 1–31.CrossRefGoogle Scholar
Sternberg, R.J. (1993). Lecture at Johns Hopkins University.Google Scholar
Stroustrup, B. (1992). The C++ programming language, 2nd ed. Addison-Wesley, Reading.Google Scholar
Sycara, K.P., & Navinchandra, D. (1991). Influences: A thematic abstraction for creative use of multiple cases, in Case-Based Reasoning Workshop, pp. 133–133, DARPA, Washington, D.C.Google Scholar
Taguchi, G., & Wu, Y. (1980). Introduction to off-line quality control. Central Japan Quality Association, Nagoya, Japan.Google Scholar
Tomas, V. (1958). Creativity in art. Philos. Rev. 67.CrossRefGoogle Scholar
Visser, J.. (1988). Giving up a hierarchical plan in a design activity (Report No. 814), Rocquencourt: INRIA.Google Scholar
Visser, J. (1990). More or less following a plan during design: Opportunistic deviations in specification. Int. J. Man-Machine Stud. 33, 247–278.CrossRefGoogle Scholar
Visser, J. (1991). The cognitive psychology viewpoint in design: Examples from empirical studies. In Proc. Artif. Intelli. Design '91, (Gero, J.S., Ed.), Butterworth-Heinemann Ltd., Oxford.Google Scholar
Wallas, G. (1970). The art of thought. In Creativity: Selected Readings, (Vernon, P.E., Ed.), Penguin Harmondsworth, NJ.Google Scholar
Watson, J.D. (1968) The double helix: A personal account of the discovery of DNA. Signet Books, New York.Google Scholar
Weisberg, R.W. (1986). Creativity: Genius and other myths. W.H. Freeman, New York.Google Scholar
Werkman, K.J. (1992). Multiple agent cooperative design evaluation using negotiation. In Artificial Intelligence in Design '92, (Gero, J.S. and Sudweeks, F., Eds.), Kluwer Academic Publishers, London.Google Scholar
Wertheimer, M. (1945). Productive thinking. Harper, New York.Google Scholar
Whitehead, A.N. (1925). An enquiry concerning the principles of natural knowledge.Google Scholar
Wilensky, R. (1978). Understanding goal-based stories. Ph.D. dissertation (Report No. 140), Yale University, New Haven, CT.Google Scholar
Williams, P. (1993). The great synthesizer. American Society for Engineering Education, PRISM, December.Google Scholar
Winston, P.H. (1984). Artificial Intelligence. Addison-Wesley, New York.Google Scholar
Zhao, F. (1991). A knowledge-based representation of mutation for creative design. Ph.D. dissertation, Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
Zhao, F., & Maher, M.L. (1992). Using network-based prototypes to support creative design by analogy and mutation. In Artificial Intelligence in Design '92, (Gero, J.S. and Sudweeks, R. Eds.), Kluwer Academic Publishers, London.Google Scholar