Explanation-Based Learning: A survey

• Aben, M. W. M. M. (1989) Knowledge base refinement: A logic-programming approach. Technical Report 1016 BS Amsterdam, Dept. of Social Science Informatics, University of Amsterdam.

• Bareiss, E. R., Porter, B. W. and Wier, C. C. (1989) Protos: An exemplar-based learning apprentice. In Y. Kodratoff and R. S. Michalski, (eds.), Machine Learning: An Artificial Intelligence Approach, Volume 3, Morgan Kaufmann.

• Barr, A. and Feigenbaum, E. (Eds.) (1981) The Handbook of Artificial Intelligence, Addison Wesley, pp. 216 ff.

• Borgida, A., Mitchell, T. and Williamson, K. E. (1985) Learning improved integrity constraints and schemas from exceptions in data and knowledge bases, in M. L. Brodie and J. Mylopoulos (Eds.), On Knowledge Based Management Systems, NY, Springer Verlag.

  Google Scholar 

• Buchanan, B. G. and Mitchell, T. (1978) Model-directed learning of production rules. In Pattern-Directed Inference Systems, pp. 297–312, New York, Academic Press.

  Google Scholar 

• Carbonell, J. G., Michalski, R. S. and Mitchell, T. M. (1985) An overview of machine learning, in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Volume 1, pp. 3–24. Tioga Publishing Company.

• Chien, S. (1988) A framework for explanation-based refinement, Proceedings of The National Conference On Artificial Intelligence Spring Symposium Series on Explanation-Based Learning, pp. 137–141.

• Clocksin, W. F. and Mellish, C. S. (1981) Programming in Prolog, Springer-Verlag.

• Danyluk, A. P. (1987) The use of explanations for similarity-based learning, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 274–276, Milan, Italy.

• Davis R. and Lenat D. (1982) Knowledge-Based Systems in Artificial Intelligence, McGraw Hill, NY.

  Google Scholar 

• DeJong, G. and Mooney, R. (1986) Explanation-based generalization: An alternative view, Machine Learning 1, 145–176.

  Google Scholar 

• DeJong, G. (1981) Generalization based on explanations, in Proceedings of the Seventh International Joint Conference of Artificial Intelligence, Vancouver, British Columbia, Canada, Morgan Kaufmann.

  Google Scholar 

• DeJong, G. (1988) Some thoughts on the present and future of explanation-based learning, in Proceedings of the European Conference on Artificial Intelligence, pp. 690–697, Munich.

• Dietterich, T. G. (1987) A knowledge level analysis of learning programs. 87-30-4, Oregon State University Computer Science Dept.

• Ellman, T. (1989) Explanation-based learning: A survey of programs and perspectives, ACM Computing Surveys 21(2), 163–222.

  Google Scholar 

• Fikes, R. E., Hart, P. E. and Nilsson J. (1972) Learning and executing generalized robot plans, Readings in AI.

• Flann, N. S. and Dietterich, T. G. (1986) Selecting appropriate representations for learning from examples, in Proceedings of the National Conference on Artificial Intelligence, pp. 460–466, Philadelphia, PA.

• Futamura, Y. (1971) Partial evaluation of computation process — an approach to a compilercompiler, Syst. Comput. Control 2(5), 45–50.

  Google Scholar 

• Giordana, A. and Saitta, L. (1989) Use of deep theory to refine a knowledge base, in International Joint Conference on Artificial Intelligence: Workshop on Symbolic Problem Solving in Noisy, Novel and Uncertain Task Environments, Detroit.

• Haas, N. and Hendrix, G. G. (1983) Learning by being told: Acquiring Knowledge for Information Management.

• Hall, R. J. (1988) Learning by failing to explain: using partial explanations to learn in incomplete or intractable domains, Machine Learning 13(1), 45–77.

  Google Scholar 

• Haugeland, J. (1985) Artificial Intelligence: The Very Idea, MIT Press.

• Kedar-Cabelli, S. T. and McCarty, L. (1987) Explanation-based generalization as resolution theorem proving, in Proceedings of the Fourth Machine Learning Workshop. Also produced as Rutgers University Technical Report, ML-TR-10.

• Kedar-Cabelli, S. (1985) Purpose-directed analogy, in Proceedings of the Seventh Annual Conference of the Cognitive Science Society, pp. 150–159, Irvine, CA.

• Keller, R. M. (1988) Defining operationality for explanation-based learning, Artificial Intelligence 35(2).

• Kodratoff, Y. and Tecucci, G. (1989) Rule learning in DISCIPLE, in Y. Kodratoff and A. Hutchinson, (Eds.), Machine and Human Learning: Advances in European Research, pp. 265–277, Horwood.

• Kodratoff, Y. (1988) Applications and Prospects of the AI Approach to Machine Learning, McGraw Hill, NY.

  Google Scholar 

• Kolodner, J. (1987) Extending problem solving capabilities through case-based inference, in Proceedings of the Fourth Machine Learning Workshop, University of California, Irvine, Morgan Kaufmann.

• Komoroski, H. J. (1982) Partial evaluation as a means for inferencing data structures in an applicative language: A theory and implementation in the case of PROLOG, in Proceedings of the Ninth Conference on the Principles of Programming Languages, pp. 225–267.

• Krawchuk, B. J. and Witten I. H. (1988) Explanation-based learning: its role in problem solving, 88-307-19, Calgary University Computer Science Dept.

• Langley, P., Simon, H. A., Bradshaw, G. L. and Zythow, J. M. (1987) Scientific Discovery: Computational Explorations of the Creative Process, MIT Press, Cambridge MA.

  Google Scholar 

• Lavrac, N. and Mozetic, I. (1989) Methods for knowledge acquisition and refinement in second generation expert systems, in European Knowledge Acquisition Workshop, EKAW'89, pp. 554–564, Paris.

• Lebowitz, M. (1986) Integrated learning: controlling explanation, Cognitive Science 10(2), 219–240.

  Google Scholar 

• Michalski, R. S. (1983) A theory and methodology of inductive learning, in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Volume 1, pp. 83–134, Tioga Publishing Company.

• Minton, S. (1985) Selectively generalizing plans for problem-solving, in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 596–599, CMU, Pitts.

  Google Scholar 

• Mitchell, T. M., Mahadevan, S. and Steinberg, L. I. (1985) LEAP: A learning apprentice for VLSI design, in International Joint Conference on Artificial Intelligence, IJCAI'85, LA, pp. 573–580.

• Mitchell, T. M., Keller, R. M. and Kedar-Cabelli, S. T. (1986) Explanation-based generalization: A unifying view, Machine Learning 1, 47–80.

  Google Scholar 

• Mitchell, T. (1983) Learning and problem solving, in Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pp. 1139–1151, Karlsruhe, W. Germany, Morgan Kaufmann.

  Google Scholar 

• Mooney, R. J. and DeJong, G. F. (1985) Learning schemata for natural language processing, in Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles.

• Mostow, J. and Bhatnagar, N. (1987) Failsafe: A floor planner that uses explanation-based learning to learn from its failures, in International Joint Conference on Artificial Intelligence, IJCAI'85, LA, pp. 249–255, Morgan Kaufmann.

• Mostow, D. J. (1983) Machine transformation of advice into a heuristic search procedure, in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Volume 1, pp. 367–404, Tioga Publishing Company.

• Nilsson, N. J. (1982) Principles of Artificial Intelligence, Springer-Verlag.

• Pazzani, M. J., Dyer, M. and Flowers, M. (1987) Using prior learning to facilitate the learning of new causal theories, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp., 277–279, Milan, Italy.

• Pazzani, M. J. (1985) Explanation and generalization-based memory, in Proceedings of the Seventh Annual Conference of the Cognitive Science Society, pp. 323–328, Irvine, CA.

• Pazzani, M. J. (1988) Explanation-based learning for knowledge-based systems, Volume 1. In B. R. Gaines and J. H. Boose (Eds.), Knowledge Acquisition for Knowledge-Based Systems, pp. 217–237, Academic Press Ltd.

• Prieditis, E. and Mostow, J. (1987) PROLEARN: towards a prolog interpreter that learns, in Proceedings of The National Conference On Artificial Intelligence '87, pp. 494–498.

• Rich, E. (1983) Artificial Intelligence, McGraw-Hill, New York.

  Google Scholar 

• Rosenbloom, P. S. and Laird, J. E. (1986) Mapping explanation-based generalization onto Soar, Proceedings of The National Conference On Artificial Intelligence '86, 1.

• Samuel, A. (1967) Some studies in machine learning using the game of checkers: II — Recent progress, IBM Journal of Research and Development 11, 601–617.

  Google Scholar 

• Segre, A. M. (1987) On the operationality/generality trade-off in EBL, in Proceedings of 10th International Joint Conference on Artificial Intelligence, IJCAI'87.

• Simon, H. (1983) Why should machines learn? In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Volume 1, pp. 25–37, Tioga Publishing Company.

• Sterling, L. and Shapiro, E. (1987) The Art of Prolog: Advanced Programming Techniques, MIT Press.

• Subramanian, D. and Smith, D. (1988) Knowledge level learning: an alternative view, in Proceedings of the AAAI Spring Symposium on Explanation-Based Learning, pp. 196–200, Menlo Park, Calif., AAAI.

  Google Scholar 

• Van de Velde, W. (1988) Learning through progressive refinement, in D. Sleeman (Ed.), Proceedings of the Third European Working Session on Learning '88, Pitman.

• Van Harmelen, F. and Bundy, A. (1988) Explanation-based generalization = partial evaluation, Artificial Intelligence 36, 401–412.

  Google Scholar 

• Waldinger, R. (1977) Achieving several goals simultaneously, Machine Intelligence 8, 1977 (Eds. Elcock, E. and Michie, D.) Ellis Horwood Ltd., London.

  Google Scholar 

• Winston, P. H., Binford, T. O., Katz, B. and Lowry, M. (1983) Learning physical descriptions from functional definitions, examples and precedents, in Proceedings of The National Conference On Artificial Intelligence, pp. 433–439, Washington DC, Morgan Kaufmann.

  Google Scholar 

• Wirth, R. (1988) Learning by failure to prove, in D. Sleeman (Ed.), Proceedings of 3rd EWSL, pp. 237–251. Pitman, London.

  Google Scholar 

• Worden, R. (1988) Processes of knowledge and software, in B. Kelly and A. Rector (Eds.), Research And Development in Expert Systems-5, Proceedings of Expert Systems '88, pp. 139–159, Cambridge University Press.

• Wusteman, J. C. (1991) EBKAT: An Explanation-Based Knowledge Acquisition Tool, Ph.D. thesis, Dept. of Computer Science, University of Exeter, Devon, UK.

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