Heuristic-based learning

  • Stuart H. Rubin
Track 2: Artificial Intelligence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 507)


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.


Case-based reasoning distributed computation machine learning transformation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [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.MathSciNetGoogle Scholar
  2. [2]
    M. Bruynooghe, L. De Raedt, and D. De Schreye. Explanation Based Program Transformation. IJCAI-89, 1989, pp. 407–412.Google Scholar
  3. [3]
    J.G. Carbonell. Derivational Analogy and its Role in Problem Solving. AAAI-83, 1983, pp. 64–69.Google Scholar
  4. [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. [5]
    T.R. Davies and S.J. Russel. A Logical Approach to Reasoning by Analogy. IJCAI-87, 1987.Google Scholar
  6. [6]
    N. Dershowitz. The Evolution of Programs. Birkhauser, Boston, MA, 1983.Google Scholar
  7. [7]
    B. Falkenhainer, K. Forbus, and D. Gentner. The Structure Mapping Engine. AAAI-86, August 1986.Google Scholar
  8. [8]
    D. Gentner. Structure-mapping: A Theoretical Framework for Analogy. Cognitive Science, 7(2), 1983, pp. 155–170.CrossRefGoogle Scholar
  9. [9]
    R. Greiner. Learning by Understanding Analogies. Artificial Intelligence, 35, 1988, pp. 81–125.MATHMathSciNetCrossRefGoogle Scholar
  10. [10]
    M.T. Harandi and S. Bhansali. Program Derivation Using Analogy. IJCAI-89, August 1989, pp. 389–394.Google Scholar
  11. [11]
    R.E. Kling. A Paradigm for Reasoning by Analogy. Artificial Intelligence, 2, 1971, pp. 147–178.MATHCrossRefGoogle Scholar
  12. [12]
    M. Manago. Knowledge Intensive Induction. Proceedings of the Sixth International Workshop on Machine Learning, 1989, pp. 151–155.Google Scholar
  13. [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. [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. [15]
    S. Minton. Learning Search Control Knowledge: An Explanation-Based Approach, Kluwer Academic Publishers, Boston, MA, 1988.Google Scholar
  16. [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.CrossRefGoogle Scholar
  17. [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. [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. [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. [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. [21]
    S.H. Rubin. Requirement-Driven Decision Support Systems. IEEE International Conference on Systems, Man, and Cybernetics, Cambridge, MA, November 1989.Google Scholar
  22. [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

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Stuart H. Rubin
    • 1
  1. 1.Central Michigan UniversityUSA

Personalised recommendations