Hostname: page-component-74656f9884-g96k6 Total loading time: 0 Render date: 2026-11-10T03:52:12.861Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning about novel operating environments: Designing by adaptive modelling

Published online by Cambridge University Press:  27 February 2009

S. Prabhakar  and
A. K. Goel
S. Prabhakar
Affiliation:
School of Computing Sciences, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia
A. K. Goel
Affiliation:
College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30332, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

According to Simon (1983), machine learning is a process that improves the performance of an intelligent system. The performance task of a design system is to arrive at a structural description of a device given its functional description. Machine learning can occur, to improve the performance of the design system, in a number of ways. The learning can make the process of arriving at a known structure to function mapping faster, either by improving the control strategy or by learning new world knowledge needed for the structure to function mapping. It can also improve the performance of the design system by allowing it to come up with new structure to function mappings. The latter kinds of learning leads to innovative designs. The work presented in this paper, EnviroAdapt, belongs to the latter category. EnviroAdapt has the performance task of designing devices along with their operating environments. The EnviroAdapt learns about the novel environments and designs devices for these environments. This learning process is enabled by adapting the previous designs of devices along with their operating environments. This adaptation process makes use of abstracting general mechanisms from the previous designs, then instantiating them in the context of new design problem. Both of these processes are driven by the characteristics of the new design problem that requires a device to operate within a novel operating environment.

Information

Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 151 - 156
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.)

Article purchase

Temporarily unavailable

References

REFERENCES

Bhatta, S., & Goel, A. (1993). Learning generic mechanisms from experiences for analogical reasoning. Proc. Fifteenth Annual Conf. Cognitive Sci. Soc, pp. 237–242.Google Scholar
Bhatta, S., Goel, A., & Prabhakar, S. (1994). Innovation in analogical design: A model-based approach. In Artificial Intelligence in Design '94 (Gero, J.S. and Sudweeks, F., Eds.), pp. 57–74. Kluwer Publishers, Netherlands.Google Scholar
Goel, A. (1991). A model-based approach to case adaptation. Proc. Thirteenth Ann. Conf. Cognitive Sci. Soc, 143–148.Google Scholar
Goel, A.K., & Chandrasekaran, B. (1989). Functional representation of designs and redesign problem solving. Proc. Eleventh Int. Joint Conf. Artif. Intell., 1388–1394.Google Scholar
Goel, A., & Prabhakar, S. (1994). A control architecture for redesign and design verification. Proc. Australian New Zealand Intell. Info. Syst. Conf., pp. 377–381. Brisbane, Queensland, Australia.Google Scholar
Prabhakar, S., & Goel, A.K. (1992). Performance driven creativity in design: Constraint discovery, model revision, and case composition. Second Int. Round-Table Conf. Computational Models of Creative Design, 101–127.Google Scholar
Prabhakar, S., Goel, A., & Bhatta, S. (1995). Adaptive modelling in the design of interactive devices: Towards a computational theory of engineering invention. Third Int. Round-Table Conf. Computational Models of Creative Design, 267–301.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. Lawrence Erlbaum, Hillsdale, NJ.Google Scholar
Simon, H.A. (1981). The sciences of artificial. The MIT Press, Cambridge, MA.Google Scholar
Simon, H.A. (1983). Why should machines learn? In Machine Learning: An Artificial Intelligence Approach (Michalski, R. S., Mitchell, T. M. and Carbonell, J., Eds.) pp. 25–37. Tioga Publishing Company, Palo Alto, CA.Google Scholar
Stroulia, E., & Goel, A.K. (1992). Generic teleological mechanisms and their use in case adaptation. Proc. Fourteenth Ann. Conf. Cognitive Sci. Soc, 319–324.Google Scholar
Stroulia, E., Shankar, M., Goel, A., & Penberthy, L. (1992). A model-based approach to blame assignment in design, Technical Report, College of Computing, Georgia Institute of Technology, Atlanta.CrossRefGoogle Scholar