Abstract
It has been observed that the addition of clauses learned by explanation-based generalization may degrade, rather than improve, the efficiency of a logic program. There are three reasons for the degradation: i) increased unification cost ii) increased inter-clause repetition of goal calls iii) increased redundancy. There have been several approaches to solve (or reduce) these problems. However, previous techniques that solve the redundancy problem do in fact increase the two first problems. Hence, the benefit of avoiding redundancy might be outweighed by the cost associated with these techniques. A solution to this problem is presented: the algorithm EGU II, which is a reformulation of one of the previous techniques (Example-Guided Unfolding). The algorithm is based upon the application of program transformation rules (definition, unfolding and folding) and is shown to preserve the equivalence of the domain theory. Experimental results are presented showing that the cost of avoiding redundancy is significantly reduced by EGU II, and that even when the redundancy problem is not present, the technique can be superior to adding clauses redundantly.
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References
Boström H., ”Eliminating Redundancy in Explanation-Based Learning”, Machine Learning: Proceedings of the 9th International Conference, Morgan Kaufmann, CA (1992) 37–42
Boström H., Efficient Organization of Clauses Learned by Explanation-Based Generalization, SYSLAB Report, Dept. of Computer and Systems Sciences, Stockholm University (1993)
Clark P. and Holte R., ”Lazy Partial Evaluation: an Integration of Explanation-Based Generalization and Partial Evaluation”, Machine Learning: Proceedings of the 9th International Conference, Morgan Kaufmann, CA (1992) 82–91
Clocksin W. F. and Mellish C. S., Programming in Prolog, Springer Verlag, Berlin Heidelberg (1981)
Cohen W. W., ”Generalizing Number and Learning from Multiple Examples in Explanation-Based Learning”, Proceedings of the Fifth International Conference on Machine Learning, Morgan Kaufmann, CA (1988) 256–269
Debray S. K., Global Optimization of Logic Programs, Ph.D. thesis, Stony Brook (1986)
Feldman R. and Subramanian D., ”Example-Guided Optimization of Recursive Domain theories”, Proceedings of Conference on Artificial Intelligence Applications, Miami Beach, Florida, IEEE (1991) 240–244
Hofstadter D. R., Godel, Escher, Bach: an Eternal Golden Braid, Penguin Books, New York (1980)
Kedar-Cabelli S. and McCarty L. T., ”Explanation-based generalization as resolution theorem proving”, Proceedings of the Fourth International Machine Learning Workshop, Morgan Kaufmann, CA (1987) 383–389
Lloyd J. W., Foundations of Logic Programming, Springer-Verlag (1987)
Minton S., Learning Effective Search Control Knowledge: An Explanation-Based Approach, Ph.D. thesis, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA (1988)
Minton S., ”Issues in the Design of Operator Composition Systems”, Proceedings of the Seventh International Conference on Machine Learning, Morgan Kaufmann, CA (1990) 304–312
Mitchell, T. M., Keller R. M. and Kedar-Cabelli S. T., ”Explanation-Based Generalization: A Unifying View”, Machine Learning 1, (1986) 47–80
Mooney R., ”The Effect of Rule Use on the Utility of Explanation-Based Learning”, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, CA (1989) 725–730
Sablon G., De Raedt L. and Bruynooghe M., ”Generalizing Multiple Examples in Explanation Based Learning”, Proceedings of the International Workshop AII 2, Reinhardsbrunn, GDR (1989) 177–183
Sahlin D., An Automatic Partial Evaluator for Full Prolog, Ph.D. thesis, Dept. of Tele-communication and Computer Systems, The Royal Institute of Technology, Stockholm (1991)
Samuelsson C. and Rayner M., ”Quantitative Evaluation of Explanation-Based Learning as an Optimization Tool for a Large Scale Natural Language System”, Proceedings of the 12th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, CA (1992) 609–615
Shavlik J. W., ”Acquiring Recursive Concepts with Explanation-Based Learning”, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, CA (1989) 688–693
Tamaki H. and Sato T., ”Unfold/Fold Transformations of Logic Programs”, Proceedings of the Second International Logic Programming Conference, Uppsala University, Uppsala, Sweden (1984) 127–138
Wogulis J. and Langley P., ”Improving Efficiency by Learning Intermediate Concepts”, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, CA (1989) 657–662
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© 1993 Springer-Verlag Berlin Heidelberg
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Boström, H. (1993). Improving example-guided unfolding. In: Brazdil, P.B. (eds) Machine Learning: ECML-93. ECML 1993. Lecture Notes in Computer Science, vol 667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56602-3_132
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DOI: https://doi.org/10.1007/3-540-56602-3_132
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