Skip to main content

Cognitive Theory as a Guide to Automating the Configuration Design Process

  • Chapter
Artificial Intelligence in Design ’98

Abstract

The automation of the design process is extremely difficult; design tasks are complex and ill-defined, and generally performed by experts who have many years’ experience. Design is a typically human endeavour, and as humans offer the only example of flexible and successful design systems, any attempt to automate the process should be informed by the theories and studies of human cognition. In this paper, the authors put forward this argument in greater depth, before presenting a general cognitive framework for one particular design task, that of configuration design, the task of selecting and connecting a set of domain components to satisfy a given set of requirements. This framework has permitted the implementation of an automated configuration design tool for the domain of fluid power systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Carbonell, J. G., Michalski, J. K. and Mitchell, T. M.: 1983, An overview of machine learning, in J. K. Michalski, J. G. Carbonell and T. M. Mitchell (eds) Machine Learning: An Artificial Intelligence Approach, Tioga, Palo Alto, CA, pp. 3–23.

    Google Scholar 

  • Clark, A.: 1990, Microcognition: Philosophy, Cognitive Science and Parallel Distributed Processing, Bradford, London, UK.

    Google Scholar 

  • Coyne, R. D., Newton, S. and Sudweeks, F.: 1997, A connectionist view of creative design reasoning, in J.S. Gero and M. L. Maher (eds), Modeling Creativity and Knowledge-Based Creative Design, Lawrence Erlbaum, NJ, pp. 177–209.

    Google Scholar 

  • Coyne, R. D., Rosenman, M. A., Radford, A. D., Balachandran and Gero, J. S.:1990, Knowledge-based Design Systems, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Dong, A. and Agogino, A. M.: 1997, Text analysis for constructing design representations, Artificial Intelligence in Engineering 11, 65–75.

    Article  Google Scholar 

  • Dreyfus, H. L. and Dreyfus, S. E.: 1986, Mind Over Machine,Free Press, NY.

    Google Scholar 

  • Fodor, J. and Pylyshyn, Z.: 1988, Connectionism and cognitive architecture: A critical analysis, Cognition, 28, 3–71.

    Article  Google Scholar 

  • Glass, A. L. and Holyoak, K. J.: 1986, Cognition, McGraw-Hill, London.

    Google Scholar 

  • Kota, S. and Lee, C. L.: 1993, General framework for configuration design: Part 1 - methodology, Journal of Engineering Design,4(4), 277–289.

    Article  Google Scholar 

  • Lippmann, R. P.: 1987, An introduction to computing with neural networks, IEEE Acoustics, Speech and Signal Processing Magazine, 4(2), 4–22.

    Google Scholar 

  • Mittal S. and Frayman F.: 1989, Towards a generic model of configuration tasks, Proceedings 11th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Francisco, pp. 1395–1401.

    Google Scholar 

  • Newell, A.: 1980, Reasoning, problem solving and decision process: The problem space as a fundamental category, in R. Nickerson (ed.), International Symposium on Attention and Performance 8, Lawrence Erlbaum, Princeton, pp. 693–719.

    Google Scholar 

  • Newell, A., Shaw, J. C. and Simon, H.: 1967, The process of creative thinking, in H. Gruber, G. Terrell and M. Wertheimer (eds), Contemporary Approaches to Creative Thinking,Atherton, New York, pp. 63–119.

    Google Scholar 

  • Newell, A. and Simon, H.: 1976, Computer science as empirical enquiry, in J. Haugeland (ed.), Mind Design, MIT Press, Cambridge, MA, pp. 35–66.

    Google Scholar 

  • Norman, D. A.: 1981, Perspectives on Cognitive Science, Ablex, Norwood, NJ.

    Google Scholar 

  • Paivio, A.: 1986, Mental Representations,Oxford University Press, Oxford.

    Google Scholar 

  • Peirce, C. S.: 1955, in J. Bucher (ed.), Philosophical Writings of Peirce,Dover, NY.

    Google Scholar 

  • Potter, S. E., Chawdhry, P. K., Culley, S. J. and Burrows, C. R.: 1997, Goal-based configuration design of fluid power systems using neural networks, Proceedings ASME Design Engineering Technical Conferences,September, Sacramento, CA.

    Google Scholar 

  • Pugh, S.: 1990, Total Design, Addison-Wesley, Wokingham, UK.

    Google Scholar 

  • Putnam, H.: 1975, The meaning of `meaning’, in H. Putnam (ed.), Mind, Language and Reality: Philosophical Papers of Hilary Putnam, vol 2, Cambridge University Press, Cambridge, pp. 215–271.

    Chapter  Google Scholar 

  • Reed, E. S.: 1994, Perception is to self as memory is to selves, in U. Neisser and R. Fivush (eds), The Remembering Self, Cambridge University Press, Cambridge, UK, pp. 278–292.

    Chapter  Google Scholar 

  • Simon, H. A.: 1996, The Sciences of the Artificial,3rd edn, MIT Press, Cambridge, MA.

    Google Scholar 

  • Simon, H. A. and Newell, A.: 1958, Heuristic problem solving: The next advance in operations research, Operations Research, 6(1), 1–10.

    Article  Google Scholar 

  • Soufi, B. and Edmonds, E.: 1996, The cognitive basis of emergence: Implications for design support, Design Studies, 17(4), 451–463.

    Article  Google Scholar 

  • Wallace, K. M. and Dwarakanath, S.: 1994, Understanding decision making in engineering design, in J. A. Powell (ed.), Information Technology to Support Engineering Decision Making, DRAL and EPSRC, pp. 40–49.

    Google Scholar 

  • Wielinga, B. and Schreiber, G.: 1997, Configuration design problem solving, IEEE Expert, March-April, 49–56.

    Google Scholar 

  • Wielinga, B., Van de Velde, W., Schreiber, A. T. and Akkermans H.: 1992, The KADS knowledge modelling approach, in R. Mizoguchi et al. (eds), Proceedings 2nd Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop, Hitachi Advanced Research Laboratory, Hatoyama, Saiama, Japan, pp. 23–42.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Darlington, M., Potter, S., Culley, S.J., Chawdhry, P.K. (1998). Cognitive Theory as a Guide to Automating the Configuration Design Process. In: Gero, J.S., Sudweeks, F. (eds) Artificial Intelligence in Design ’98. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5121-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-5121-4_11

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6153-7

  • Online ISBN: 978-94-011-5121-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics