Advertisement

The Knowledge Engineer as Student: Metacognitive Bases for Asking Good Questions

  • William J. Clancey
Part of the Cognitive Science book series (COGNITIVE SCIEN)

Abstract

A knowledge engineer can be viewed as a special kind of student. Her goal is to develop computational models of complex problem solving by watching and questioning an expert and incrementally testing her model on a set of selected problem cases.1 Characteristically, the knowledge engineer (KE) is in complete control of this process. Her construction of a problem-solving model is almost completely self-directed; she is an active learner. The KE thus provides us with an excellent basis for studying methods that any student might use for approaching new problem domains and acquiring the knowledge to solve a set of practical problems.

Keywords

Expert System Knowledge Representation Representation Language Metacognitive Knowledge Inference Procedure 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bennett, J. (1983). ROGET: A knowledge-based consultant for acquiring the conceptual structure of an expert system. HPP Memo 83–24. Stanford, CA: Stanford University.Google Scholar
  2. Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals. New York: David McKay.Google Scholar
  3. Brown, J. S. (1983). Process versus product—A perspective on tools for communal and informal electronic learning. In Education in the Electronic Age, proceedings of a conference sponsored by the Educational Broadcasting Corporation, WNET. Google Scholar
  4. Brown, J. S., & VanLehn, K. (1980). Repair theory: A generative theory of bugs in procedural skills. Cognitive Science, 4, 379–415.CrossRefGoogle Scholar
  5. Bruner, J. S. (1983). In search of mind: Essays in autobiography. New York: Harper & Row.Google Scholar
  6. Bruner, J. S. (1986). Actual minds, possible worlds. Cambridge, MA: Harvard University Press.Google Scholar
  7. Chandrasekaran, B. (1984). Expert systems: Matching techniques to tasks. In W. Reitman (Ed.) AI applications for business (pp. 116–132). Norwood, NJ: Ablex.Google Scholar
  8. Chandrasekaran, B. (1986). Proceedings of the workshop on high level tools for knowledge-based systems. Columbus, OH: Ohio State.Google Scholar
  9. Clancey, W. J. (1982). GUIDON. Applications-oriented AI research: Education. In A. Barr & E.A. Feigenbaum (Eds.), The handbook of artificial intelligence. (pp. 267–278). Los Altos, CA: Kaufmann.Google Scholar
  10. Clancey, W. J. (1983a). The advantages of abstract control knowledge in expert system design. In Proceedings of the national conference on artificial intelligence. (pp. 74–78). Washington, DC: Los Altos, CA: Morgan-Kaufmann.Google Scholar
  11. Clancey, W. J. (1983b). The epistemology of a rule-based expert system: A framework for explanation. Artificial Intelligence, 20, 215–251.CrossRefGoogle Scholar
  12. Clancey, W. J. (1984). Knowledge acquisition for classification expert systems. In Proceedings of ACM annual conference (pp. 11–14).Google Scholar
  13. Clancey, W. J. (1985). Heuristic classification. Artificial Intelligence, 27, 289–350.CrossRefGoogle Scholar
  14. Clancey, W. J. (1986a). From Guidon to Neomycin and Heracles in twenty short lessons (ONR Final Report 1979–1985). AI Magazine, 7, 40–60.Google Scholar
  15. Clancey, W. J. (1986b). Qualitative student models. In Annual Review of Computer Science, (pp. 381–450). Palo Alto, CA: Annual Reviews, Inc.Google Scholar
  16. Clancey, W. J. (1987). Knowledge-based tutoring: The Guidon program. Cambridge, MA: MIT Press.Google Scholar
  17. Clancey, W. J. (1988a) Acquiring, representing, and evaluating a competence model of diagnosis. In M. Chi, R. Glaser, & M. Farr (Eds.), The Nature of Expertise. Hillsdale: Laurence Erlbaum.Google Scholar
  18. Clancey, W. J. (1988b). Viewing knowledge bases as qualitative models. IEEE Expert, in press.Google Scholar
  19. Clancey, W. J. (in press). Representing control knowledge as abstract tasks and metarules. In M. J. Coombs, & L. Bolc (Eds.), Computer expert systems, New York: Springer-Verlag.Google Scholar
  20. Clancey, W. J., & Letsinger, R. (1984). NEOMYCIN: Reconfiguring a rule-based expert system for application to teaching. In W. J. Clancey, & E. H. Shortliffe (Eds.), Readings in medical artificial intelligence: The first decade (pp. 361–381). Reading, PA: Addison-Wesley.Google Scholar
  21. Collins, A. (1978). Fragments of a theory of human plausible reasoning. In Proceedings of the 2nd Conference on Theoretical Issues in Natural Language Processing. D. L. Waltz (ed.) Urbana-Champaign University of Illinois. Theoretical Issues in Natural Language Processing (pp. 194–201).CrossRefGoogle Scholar
  22. Crovello, T., & McDaniel, M. An artificial intelligence-based introduction to the scientific method. Unpublished manuscript.Google Scholar
  23. Davis, R., & Buchanan, B. G. (1977). Metal-level knowledge: Overview and applications. In Proceedings of the Fifth International Joint Conference on Artifical Intelligence-77 (pp. 920–927).Google Scholar
  24. DeJong, G., & Mooney, R. (1986). Explanation-based learning: An alternative view. Machine Learning, 1, 145–176.Google Scholar
  25. Dewey, J. (1964). The process and product of reflective activity: Psychological process and logical form. In R. D. Archambault (Ed.), John Dewey on education: Selected writings (pp. 243–259). New York: Random House.Google Scholar
  26. Dietterich, T. G., Flann, N. S., & Wilkins, D. C. (1986). A summary of machine learning papers from IJCAI-85. Technical Report 86–30–2, Oregon State University, Corvallis.Google Scholar
  27. Eshelman, L., Ehret, D., McDermott, J., & Tan, M. (1986). MOLE: A tenacious knowledge acquisition tool. In Proceedings of knowledge acquisition for knowledge-base systems workshop (pp. 13–1–13–12).Google Scholar
  28. Jackson, P. C. (1974). Introduction to Artificial Intelligence. New York: Petrocelli Books.Google Scholar
  29. Kahn, G., Nowlan, S., & McDermott, J. (1985). MORE: An intelligent knowledge acquisition tool. In Proceedings of the ninth international joint conference on artificial intelligence (pp. 581–584).Google Scholar
  30. Keller, R. M. (1986). Deciding what to learn. Technical Report ML-TR-6, Rutgers University, New Brunswick, NJ.Google Scholar
  31. Kolodner, J. L., & Simpson, R. L. (1984). Experience and problem solving: A framework. In Proceedings of the sixth annual conference of the Cognitive Science Society (pp. 239–243). Boulder, CO.Google Scholar
  32. Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1, 47–80.Google Scholar
  33. Mitchell, T. M., Mahadevan, S., & Steinberg, L. I. (1985). LEAP: A learning apprentice for VLSI design. In Proceedings of the ninth international joint conference on artificial intelligence (pp. 573–580).Google Scholar
  34. Patil, R. S., Szolovits, P., & Schwartz, W. B. (1981). Causal understanding of patient illness in medical diagnosis. In Proceedings of the seventh international joint conference and artificial intelligence (pp. 893–899).Google Scholar
  35. Richer, M. H., & Clancey, W. J. (1985). GUIDON-WATCH: A graphic interface for viewing a knowledge-based system. IEEE Computer Graphics and Applications, 5, 51–64.CrossRefGoogle Scholar
  36. Rodolitz, N. (1987). Tutoring for Strategic Knowledge. KSL Report 87–38. Stanford University.Google Scholar
  37. Schank, R. C. (1981). Failure-driven memory. Cognition and Brain Theory, 4, 41–60.Google Scholar
  38. Schoenfeld, A. H. (1981, April). Episodes and executive decisions in mathematical problem solving. Technical Report, Hamilton College, Mathematics Department. Presented at the 1981 AERA Annual Meeting.Google Scholar
  39. Smith, R. G., Winston, H. A., Mitchell, T. M., & Buchanan, B. G. (1985). Representation and use of explicit justifications for knowledge base refinement. In Proceedings of the ninth international joint conference on artificial intelligence (pp. 673–680).Google Scholar
  40. Thompson, T., & Clancey, W. J. (1986). A qualitative modeling shell for process diagnosis. IEEE Software, 3, p. 6–15.CrossRefGoogle Scholar
  41. VanLehn, K. (1987). Learning one subprocedure per lesson. Artificial Intelligence, 31, 1–40.CrossRefGoogle Scholar
  42. Weiss, S. M., Kulikowski, C. A., Amarei, S., & Safir, A. (1978). A model-based method for computer-aided medical decision making. Artificial Intelligence, 11, 145–172.CrossRefGoogle Scholar
  43. Wilkins, D. C., Clancey, W. J., & Buchanan, B. G. (1986). An overview of the Odysseus learning apprentice. In T. M. Mitchell, J. G. Carbonell, & R. S. Michalski (Eds.), Machine learning: A guide to current research. Orlando, FL: Academic Press.Google Scholar
  44. Winograd, T., & Flores, C. F. (1985). Understanding computers and cognition: A new foundation for design. Norwood, NJ: Ablex.Google Scholar
  45. Whorf, B. L. (1956). Language, thought, and reality, J. B. Carroll (Ed.). New York.Google Scholar

Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • William J. Clancey

There are no affiliations available

Personalised recommendations