Synonyms
Definition
Explanation-Based Learning (EBL) is a principled method for exploiting available domain knowledge to improve supervised learning. Improvement can be in speed of learning, confidence of learning, accuracy of the learned concept, or a combination of these. In modern EBL the domain theory represents an expert’s approximate knowledge of complex systematic world behavior. It may be imperfect and incomplete. Inference over the domain knowledge provides analyticevidence that compliments the empirical evidence of the training data. By contrast, in original EBL the domain theory is required to be much stronger; inferred properties are guaranteed. Another important aspect of modern EBL is the interaction between domain knowledge and labeled training examples afforded by explanations. Interaction allows the nonlinear combination of evidence so that the resulting information about the target concept can be much...
This is a preview of subscription content, log in via an institution to check access.
Recommended Reading
Anderson, J. (1986). Knowledge compilation: The general learning mechanism. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning II (pp. 289–310). San Mateo, CA: Morgan Kaufmann.
Bruynooghe, M., De Raedt, L., & De Schreye, D. (1989). Explanation based program transformation. In IJCAI (pp. 407–412).
Cohen, W. W. (1992). Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem. Machine Learning, 8, 167–219.
DeJong, G. (1981). Generalizations based on explanations. In IJCAI’81, the seventh international joint conference on artificial intelligence (pp. 67–69). Vancover, BC.
DeJong, G. (2006). Toward robust real-world inference: A new perspective on explanation-based learning. In ECML06, the seventeenth European conference on machine learning (pp. 102–113). Heidelberg: Springer.
DeJong, G., & Mooney, R. (1986). Explanation-based learning: An alternative view. Machine Learning, 1(2), 145–176.
Etzioni, O. (1993). A structural theory of explanation-based learning. Artificial Intelligence, 60(1), 93–139.
Fikes, R., Hart, P. E., & Nilsson, N. J. (1972). Learning and executing generalized robot plans. Artificial Intelligence, 3(1–3), 251–288.
Flann, N. S., & Dietterich, T. G. (1989). A study of explanation-based methods for inductive learning. Machine Learning, 4, 187–226.
Freund, Y., Schapire, R. E., Singer, Y., & Warmuth, M. K. (1997). Using and combining predictors that specialize. In Twenty-ninth annual ACM symposium on the theory of computing (pp. 334–343). El Paso, TX.
Genest, J., Matwin, S., & Plante, B. (1990). Explanation-based learning with incomplete theories: A three-step approach. In proceedings of the seventh international conference on machine learning (pp. 286–294).
Gratch, J., & DeJong, G. (1992). Composer: A probabilistic solution to the utility problem in speed-up learning. In AAAI (pp. 235–240).
Greiner, R., & Jurisica, I. (1992). A statistical approach to solving the EBL utility problem. In National conference on artificial intelligence (pp. 241–248). San Jose, CA.
Hirsh, H. (1987). Explanation-based generalization in a logic-programming environment. In IJCAI (pp. 221–227). Milan, Italy.
Kimmig, A., De Raedt, L., & Toivonen, H. (2007). Probabilistic explanation based learning. In ECML’07, the eighteenth European conference on machine learning (pp. 176–187).
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986). Chunking in soar: The anatomy of a general learning mechanism. Machine Learning, 1(1), 11–46.
Lim, S. H., Wang, L.-L., & DeJong, G. (2007). Explanation-based feature construction. In IJCAI’07, the twentieth international joint conference on artificial intelligence (pp. 931–936)
McCarthy, J. (1980). Circumscription – a form of non-monotonic reasoning. Artificial Intelligence, 13, 27–39.
Minton, S. (1990). Quantitative results concerning the utility of explanation-based learning. Artificial Intelligence, 42(2–3), 363–391.
Mitchell, T. (1997). Machine learning. New York: McGraw-Hill.
Mitchell, T., Keller, R., & Kedar-Cabelli, S. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1(1), 47–80.
Ourston, D., & Mooney, R. J. (1994). Theory refinement combining analytical and empirical methods. Artificial Intelligence, 66(2), 273–309.
Pazzani, M. J., & Kibler, D. F. (1992). The utility of knowledge in inductive learning. Machine Learning, 9, 57–94.
Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall.
Russell, S. J., & Grosof, B. N. (1987). A declarative approach to bias in concept learning. In AAAI (pp. 505–510). Seattle, WA.
Sun, Q., & DeJong, G. (2005). Feature kernel functions: Improving svms using high-level knowledge. In CVPR (2) (pp. 177–183)
Thrun, S., & Mitchell, T. M. (1993). Integrating inductive neural network learning and explanation-based learning. In IJCAI (pp. 930–936). Chambery, France.
Towell, G. G., Craven, M., & Shavlik, J. W. (1991). Constructive induction in knowledge-based neural networks. In proceedings of the eighth international conference on machine learning (pp. 213–217)
Zelle, J. M., & Mooney, R. J. (1993). Combining Foil and EBG to speed-up logic programs. In IJCAI (pp. 1106–1113). Chambery, France.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
DeJong, G., Lim, S.H. (2011). Explanation-Based Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_296
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_296
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering