UT ML Group: Explanation-Based Learning (EBL)

Most machine learning is focused on inductive generalization from empirical data and does not explicitly exploit prior knowledge of the domain. Explanation-based learning is a radically different approach that uses existing declarative domain knowledge to "explain" individual examples and uses this explanation to drive a knowledge-based generalization of the example. It is therefore capable of learning a very general concept from only a single training example. Our work was some of the original research on this approach and lead to our subsequent work on theory refinement and on learning for planning and problem-solving.

Publications

  1. Integrating EBL and ILP to Acquire Control Rules for Planning [Abstract] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), pp. 271-279, Harpers Ferry, WV, May 1996.

  2. Integrating ILP and EBL [Abstract] [PDF]
    Raymond J. Mooney and John M. Zelle
    SIGART Bulletin, Volume 5, Number 1, pp. 12-21, January 1994.

  3. Combining FOIL and EBG to Speed-Up Logic Programs [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 1106-1111, Chambéry, France, July 1993.

  4. Integrating Theory and Data in Category Learning [Abstract] [PDF]
    Raymond J. Mooney
    Categorization by Humans and Machines: The Psychology of Learning and Motivation, Vol. 29, G. Nakamura, R. Taraban, & D.L. Medin (Eds.), pp. 189-218, Academic Press, Orlando, FL, 1993.

  5. Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning [Abstract] [PDF]
    Raymond J. Mooney
    Machine Learning, 10, 1 (1993), pp. 79-110.

  6. Speeding-up Logic Programs by Combining EBG and FOIL [Abstract] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.

  7. Schema acquisition from a single example [Abstract] [PDF]
    Ahn, W., Brewer, W. F., and Mooney, R. J
    In Journal of Experimental Psychology: Learning, Memory, and Cognition , 18, 391-412, 1992.

  8. Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition [Abstract] [PDF]
    Raymond J. Mooney
    Cognitive Science, 14, 4 (1990), pp. 483--509.

  9. The Effect of Rule Use on the Utility of Explanation-Based Learning [Abstract] [PDF]
    Raymond J. Mooney
    In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI), pp. 725-730, Detroit, MI, August 1989.

  10. Generalizing the Order of Operators in Macro-Operators [Abstract] [PDF]
    Raymond J. Mooney
    In Proceedings of the Fifth International Conference on Machine Learning (ICML), pp. 270-283, Ann Arbor, MI: Morgan Kaufmann, June 1988.

  11. Integrated Learning of Words and their Underlying Concepts [Abstract] [PDF]
    Raymond J. Mooney
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society (CogSci), pp. 974-978, Seattle, WA, July 1987.

  12. Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning [Abstract] [PDF]
    Ahn, W., Mooney, R.J., Brewer, W.F., DeJong, G.F.
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society (CogSci), pp. 50-57, Seattle, WA, July 1987.

  13. Explanation-Based Learning: An Alternative View [Abstract] [PDF]
    DeJong, G.F. and Mooney, R.J.
    Machine Learning 1, 2 (1986), pp. 145--176. (reprinted in in Readings in Machine Learning, J. W. Shavlik and T. G. Dietterich (eds.), Morgan Kaufman, San Mateo, CA 1990)

  14. A Domain Independent Explanation-Based Generalizer [Abstract] [PDF]
    Raymond J. Mooney and S.W. Bennett
    In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI), pp. 551-555, Philadelphia, PA, August 1986.

  15. Learning Schemata for Natural Language Processing [Abstract] [PDF]
    Raymond J. Mooney and Gerald F. DeJong
    In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI), pp. 681-687, Los Angeles, CA, August 1985.

mooney@cs.utexas.edu