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Heuristic-based learning

  • Track 2: Artificial Intelligence
  • Conference paper
  • First Online:
Computing in the 90's (Great Lakes CS 1989)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 507))

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Abstract

Knowledge-based systems are becoming increasingly model oriented. Models enable the system a deeper understanding — something which is impractical to attain when all the system has are rules. Furthermore, it has become apparent that knowledge representations must become increasingly domain-specific in order to facilitate more sophisticated problem solving. The task of automating the solution of sophisticated problems in turn implies the use of analogic reasoning towards the goal of automatic knowledge acquisition.

The approach taken here is to investigate new machine learning algorithms focusing on lateral model-based transformative induction methods similar to Quinlan's ID3 and Michalski's AQ algorithms — except that models are the generalized object(s) rather than simply decision trees or rules.

Funding for this project was provided by the Office of Naval Technology (ONT) Postdoctoral Fellowship Program, Projects Office, ASEE, 11 Dupont Circle, Suite 200, Washington, DC 20036

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References

  1. H. Abelson, M. Eisenberg, M. Halfant, J. Katzenelson, E. Sacks, G.J. Sussman, J. Wisdom, and K. Yip. Intelligence in Scientific Computing. CACM, 32(5), May 1989, pp. 546–562.

    MathSciNet  Google Scholar 

  2. M. Bruynooghe, L. De Raedt, and D. De Schreye. Explanation Based Program Transformation. IJCAI-89, 1989, pp. 407–412.

    Google Scholar 

  3. J.G. Carbonell. Derivational Analogy and its Role in Problem Solving. AAAI-83, 1983, pp. 64–69.

    Google Scholar 

  4. J.G. Carbonell. Introduction: Paradigms for Machine Learning. Artificial Intelligence, Special Volume on Machine Learning, 40(1–3), September 1989, pp. 1–9.

    Google Scholar 

  5. T.R. Davies and S.J. Russel. A Logical Approach to Reasoning by Analogy. IJCAI-87, 1987.

    Google Scholar 

  6. N. Dershowitz. The Evolution of Programs. Birkhauser, Boston, MA, 1983.

    Google Scholar 

  7. B. Falkenhainer, K. Forbus, and D. Gentner. The Structure Mapping Engine. AAAI-86, August 1986.

    Google Scholar 

  8. D. Gentner. Structure-mapping: A Theoretical Framework for Analogy. Cognitive Science, 7(2), 1983, pp. 155–170.

    Article  Google Scholar 

  9. R. Greiner. Learning by Understanding Analogies. Artificial Intelligence, 35, 1988, pp. 81–125.

    Article  MATH  MathSciNet  Google Scholar 

  10. M.T. Harandi and S. Bhansali. Program Derivation Using Analogy. IJCAI-89, August 1989, pp. 389–394.

    Google Scholar 

  11. R.E. Kling. A Paradigm for Reasoning by Analogy. Artificial Intelligence, 2, 1971, pp. 147–178.

    Article  MATH  Google Scholar 

  12. M. Manago. Knowledge Intensive Induction. Proceedings of the Sixth International Workshop on Machine Learning, 1989, pp. 151–155.

    Google Scholar 

  13. R.S. Michalski and R.L. Chilausky. Learning by being told and learning from examples. International Journal of Policy Analysis and Information Systems, 4(2), 1980, pp. 125–161.

    Google Scholar 

  14. R.S. Michalski, J.G. Carbonell, and T.M. Mitchell. Machine Learning, Volume II. Morgan Kaufman Publishers, Los Altos, CA, 1986.

    Google Scholar 

  15. S. Minton. Learning Search Control Knowledge: An Explanation-Based Approach, Kluwer Academic Publishers, Boston, MA, 1988.

    Google Scholar 

  16. S. Minton, J.G. Carbonell, C.A. Knoblock, D.R. Kuokka, O. Etzioni, and Y. Gil. Explanation-Based Learning: A Problem Solving Perspective. Artificial Intelligence, Special Volume on Machine Learning, 40(1–3), September 1989, pp. 63–118.

    Article  Google Scholar 

  17. T.M. Mitchell. "Version Spaces: A Candidate Elimination Approach to Rule Learning", in Proceedings Fifth International Joint Conference on Artificial Intelligence, 1977, pp. 305–310.

    Google Scholar 

  18. T.M. Mitchell, R. Keller, and S. Kedar-Cabelli. Explanation-Based Generalization: A Unifying View. Machine Learning, 1(1), 1986, pp. 47–80.

    Google Scholar 

  19. Office of Naval Technology. Post Doctoral Fellowship Program, 1987–88, ASEE, Projects Office, 11 Dupont Circle, Suite 200, Washington, DC 20036.

    Google Scholar 

  20. B.W. Porter. Similarity Assessment: Computation Vs. Representation. Proceedings of a Workshop on Case-Based Reasoning, Pensacola Beach, FL, June 1989, pp. 82–84.

    Google Scholar 

  21. S.H. Rubin. Requirement-Driven Decision Support Systems. IEEE International Conference on Systems, Man, and Cybernetics, Cambridge, MA, November 1989.

    Google Scholar 

  22. M.M. Veloso and J.G. Carbonell. Learning Analogies by Analogy — The Closed Loop of Memory Organization and Problem Solving. Proceedings of a Workshop on Case-Based Reasoning, Pensacola Beach, FL, June 1989, pp. 153–158.

    Google Scholar 

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Authors and Affiliations

  1. Central Michigan University, USA

    Stuart H. Rubin

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  1. Stuart H. Rubin
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Naveed A. Sherwani Elise de Doncker John A. Kapenga

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© 1991 Springer-Verlag Berlin Heidelberg

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Rubin, S.H. (1991). Heuristic-based learning. In: Sherwani, N.A., de Doncker, E., Kapenga, J.A. (eds) Computing in the 90's. Great Lakes CS 1989. Lecture Notes in Computer Science, vol 507. Springer, New York, NY. https://doi.org/10.1007/BFb0038471

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  • DOI: https://doi.org/10.1007/BFb0038471

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97628-0

  • Online ISBN: 978-0-387-34815-5

  • eBook Packages: Springer Book Archive

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