Using the System-Model-Operator Metaphor for Knowledge Acquisition

  • William J. Clancey
  • Monique Barbanson
Conference paper

Abstract

The systems-model-operator perspective provides a unifying perspective for the ways that expert systems represent, organize, and apply knowledge representations. We use this metaphor to develop Topo, an expert system for configuration of computer networks. Generalizing Topo, we show that its modeling language and operators can be adapted to other tasks that require relating a physical/organizational structure to a service-supply network. This experiment demonstrates how expert systems can be generalized and more easily related to each other if we express control knowledge in terms of operators for constructing system models.

Keywords

Topo Metaphor Neomycin 

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References

  1. 1.
    Chandrasekaran, B.: “Expert systems: Matching techniques to tasks,” in AI Applications for Business, W. Reitman, ed., Ablex Publishing, Norwood, NJ, (1984) 116–132.Google Scholar
  2. 2.
    Clancey, W. J.: Heuristic classification, Artificial Intelligence 27 (1985) 289–350.CrossRefGoogle Scholar
  3. 3.
    Clancey, W. J.: Model Construction Operators, Artificial Intelligence 53 (1992) 1–115.CrossRefGoogle Scholar
  4. 4.
    Hayes-Roth, B., Hewett, M., Vaughan Johnson, M., and Garvey, A.: “ACCORD: A Framework for a Class of Design Tasks,” Tech. Report KSL-88-19, Stanford Univ., Palo Alto, CA (1988).Google Scholar
  5. 5.
    Marcus, S.: Automating Knowledge Acquisition for Expert Systems, Kluwer Academic Publishers, (1988).Google Scholar
  6. 6.
    McDermott, J.: “Preliminary steps toward a taxonomy of problem-solving methods,” in Automating Knowledge Acquisition for Expert Systems, S. Marcus, ed., Kluwer Academic Publishers, Norwood, MA (1988) 225–256.Google Scholar
  7. 7.
    McDermott, J.: R1: A rule-based configurer of computer systems, Artificial Intelligence 19 (1982) 39–88.CrossRefGoogle Scholar
  8. 8.
    Musen, M.: Automated support for building and extending expert models, Machine Learning 4 (1989) 347–377.Google Scholar
  9. 9.
    Thompson, T. and Clancey, W.J.: A qualitative modeling shell for process diagnosis, IEEE Software 3 (1986) 6–15.CrossRefGoogle Scholar
  10. 10.
    van Melle, W.: A Domain-Independent System that Aids in Constructing Knowledge-Based Consultation Pprograms, doctoral dissertation, Stanford Univ., Computer Science Department, Palo Alto, CA, 1980.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • William J. Clancey
    • 1
  • Monique Barbanson
    • 2
  1. 1.Institute for Research on LearningPalo AltoUSA
  2. 2.Metaphor Computer SystemsUSA

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