Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Explanation-Based Learning

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.

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  1. TellMeWhy: A Dataset for Answering Why-Questions in Narratives
    [Details] [PDF] [Slides (PDF)] [Video]
    Yash Kumar Lal, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
    In Findings of ACL 2021, August 2021.
  2. Incorporating Textual Resources to Improve Visual Question Answering
    [Details] [PDF] [Slides (PDF)]
    Jialin Wu
    September 2021. Ph.D. Proposal.
  3. Stacking With Auxiliary Features for Visual Question Answering
    [Details] [PDF] [Poster]
    Nazneen Fatema Rajani, Raymond J. Mooney
    In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2217-2226, 2018.
  4. Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2012. 170.
  5. Integrating EBL and ILP to Acquire Control Rules for Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), 271--279, Harpers Ferry, WV, May 1996.
  6. Integrating ILP and EBL
    [Details] [PDF]
    Raymond J. Mooney and John M. Zelle
    Sigart Bulletin (special issue on Inductive Logic Programmming), 5(1):12-21, 1994.
  7. Combining FOIL and EBG to Speed-Up Logic Programs
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1106-1111, 1993. San Francisco, CA: Morgan Kaufmann.
  8. Integrating Theory and Data in Category Learning
    [Details] [PDF]
    Raymond J. Mooney
    In G. V. Nakamura and D. L. Medin and R. Taraban, editors, Categorization by Humans and Machines, 189-218, 1993.
  9. Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning
    [Details] [PDF]
    Raymond J. Mooney
    Machine Learning, 10:79-110, 1993.
  10. Schema acquisition from a single example
    [Details] [PDF]
    W. Ahn, W. F. Brewer and Raymond J. Mooney
    Journal of Experimental Psychology: Learning, Memory, and Cognition, 18:391-412, 1992.
  11. Speeding-up Logic Programs by Combining EBG and FOIL
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.
  12. Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition
    [Details] [PDF]
    Raymond J. Mooney
    Cognitive Science, 14(4):483-509, 1990.
  13. The Effect of Rule Use on the Utility of Explanation-Based Learning
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the 11th International Joint Conference on Artificial Intelligence, 725-730, 1989. San Francisco, CA: Morgan Kaufmann.
  14. Generalizing the Order of Operators in Macro-Operators
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Fifth International Conference on Machine Learning (ICML-88), 270-283, Ann Arbor, MI, June 1988.
  15. A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding
    [Details] [PDF]
    Raymond J. Mooney
    Ph.D. thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1988
  16. Integrated Learning of Words and their Underlying Concepts
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 947-978, Seattle, WA, July 1987.
  17. Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning
    [Details] [PDF]
    W. Ahn, Raymond J. Mooney, W.F. Brewer and G.F. DeJong
    In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 50-57, Seattle, WA, July 1987.
  18. A Domain Independent Explanation-Based Generalizer
    [Details] [PDF]
    Raymond J. Mooney and S.W. Bennett
    In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), 551-555, Philadelphia, PA, August 1986.
  19. Explanation-Based Learning: An Alternative View
    [Details] [PDF]
    G.F. DeJong and Raymond J. Mooney
    Machine Learning:145-176, 1986.
  20. Learning Schemata for Natural Language Processing
    [Details] [PDF]
    Raymond J. Mooney and Gerald F. DeJong
    In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), 681-687, Los Angeles, CA, August 1985.
  21. Generalizing Explanations of Narratives into Schemata
    [Details] [PDF]
    Raymond J. Mooney
    Masters Thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1985.