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Machine Learning

, Volume 4, Issue 3–4, pp 285–291 | Cite as

The Knowledge Level Reinterpreted: Modeling How Systems Interact

  • William J. Clancey
Article

Keywords

Artificial Intelligence System Interact Computing Methodology Knowledge Level Language Translation 
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.

References

  1. Bennett, J., Creary, L., Engelmore, R., and Melosh, R. 1978. SACON: A knowledge-based consultant for struc-tural analysis.(STAN-CS–78-699 and HPP Memo 78-23). Stanford University, Stanford, CA.Google Scholar
  2. Brown, J. S., Burton, R. R., and De Kleer, J. 1982. Pedagogical, natural language, and knowledge engineering techniques in SOPHIE I, II, and HI. In D. Sleeman and J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic Press.Google Scholar
  3. Clancey. W. J. 1983. The advantages of abstract control knowledge in expert system design. Proceedings of the National Conference on Artificial Intelligence (pp. 74–78).Google Scholar
  4. Clancey, W. J. 1985. Heuristic classification. Artificial Intelligence, 27, 289–350.Google Scholar
  5. Clancey, W. J. 1989. Viewing knowledge bases as qualitative models. IEEE Expert, 4, 9–23.Google Scholar
  6. Clancey, W. J.(in preparation). The frame of reference problem in the design of intelligent machines. In K. van Lehn (Ed.), Architectures for intelligence: The twenty-second Carnegie symosium on cognition. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  7. Newell, A. 1982. The knowledge level. Artificial Intelligence, 18, 87–127.Google Scholar

Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • William J. Clancey
    • 1
  1. 1.Institute for Research on LearningPalo Alto

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