Decompiling Problem-Solving Experience to Elucidate Representational Distinctions

  • Jeffrey C. Schlimmer
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 87)


General, domain-independent problem-solving methods are highly flexible if inefficient. Recent work addressing the utility of learned knowledge improves efficiency, but flexibility is greatly compromised. In this paper I discuss an alternative that extracts relevant distinctions from problem-solving traces and creates explicit representational terms for them. The new terms are seamlessly integrated into declarative knowledge and are effectively utilized in subsequent problem solving.


Problem Solver Declarative Knowledge Interesting Object Domain Object Conceptual Cluster 
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.


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  1. [DeJong and Mooney, 1986]
    G. DeJong and R. Mooney. Explanation-based learning: An alternative view. Machine Learning, 1(2):145–176, 1986.Google Scholar
  2. [Falkenhainer et al., 1986]
    B. Falkenhainer, K. D. Forbus, and D. Gentner. The Structure-Mapping Engine. In Proceedings of the 5th AAAI, pages 272–277, Morgan Kaufmann, Philadelphia, PA, 1986.Google Scholar
  3. [Fikes et al., 1972]
    R. Fikes, P. Hart, and N. Nilsson. Learning and executing generalized robot plans. Artificial Intelligence, 3(4):251–288, 1972.CrossRefGoogle Scholar
  4. [Fisher, 1987]
    D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2):139–172, 1987.Google Scholar
  5. [Forgy, 1979]
    C. L. Forgy. On the Efficient Implementation of Production Systems. Technical Report CMU-CS-79-107, Pittsburgh, PA: Carnegie Mellon University, School of Computer Science, 1979.Google Scholar
  6. [Michalski and Stepp, 1983]
    R. S. Michalski and R. E. Stepp. Learning from observation: Conceptual clustering. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, San Mateo, CA, 1983.Google Scholar
  7. [Minton, 1985]
    S. Minton. Selectively generalizing plans for problem-solving. In Proceedings of the 9th IJCAI, pages 596–599, Morgan Kaufmann, Los Angeles, CA, 1985.Google Scholar
  8. [Minton, 1988]
    S. Minton. Quantitative results concerning the utility of explanation-based learning. In Proceedings of the 7th AAAI, pages 564–569, Morgan Kaufmann, Seattle, WA, 1988.Google Scholar
  9. [Mitchell et al., 1986]
    T. M. Mitchell, R. M. Keller, and S. T. Kedar-Cabelli. Explanation-based generalization: A unifying view. Machine Learning, 1(1):47–80, 1986.Google Scholar
  10. [Mitchell et al.]
    T. M. Mitchell, J. Allen, P. Chalasani, J. Cheng, O. Etzioni, M. Ringuette, and J. C. Schlimmer. Theo: A framework for self-improving systems. In K. VanLehn, editor, Architectures for Intelligence, Earlbaum, Hillsdale, NJ.Google Scholar
  11. [Utgoff, 1986]
    P. E. Utgoff. Machine Learning of Inductive Bias. Kluwer, Boston, MA, 1986.CrossRefGoogle Scholar
  12. [Wogulis and Langley, 1989]
    J. Wogulis and P. Langley. Improving efficiency by learning intermediate concepts. In Proceedings of the 8th AAAI, Morgan Kaufmann, Detroit, MI, 1989.Google Scholar

Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Jeffrey C. Schlimmer
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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