Decompiling Problem-Solving Experience to Elucidate Representational Distinctions
General, domain-independent problem-solving methods are highly flexible if inefficient. Recent work addressing the utility of learned knowledge improves efficiency, but flexibility is greatly compromised. In this paper I discuss an alternative that extracts relevant distinctions from problem-solving traces and creates explicit representational terms for them. The new terms are seamlessly integrated into declarative knowledge and are effectively utilized in subsequent problem solving.
KeywordsTate IleAl Mellon bitO
Unable to display preview. Download preview PDF.
- [DeJong and Mooney, 1986]G. DeJong and R. Mooney. Explanation-based learning: An alternative view. Machine Learning, 1(2):145–176, 1986.Google Scholar
- [Falkenhainer et al., 1986]B. Falkenhainer, K. D. Forbus, and D. Gentner. The Structure-Mapping Engine. In Proceedings of the 5th AAAI, pages 272–277, Morgan Kaufmann, Philadelphia, PA, 1986.Google Scholar
- [Fikes et al., 1972]
- [Fisher, 1987]D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2):139–172, 1987.Google Scholar
- [Forgy, 1979]C. L. Forgy. On the Efficient Implementation of Production Systems. Technical Report CMU-CS-79-107, Pittsburgh, PA: Carnegie Mellon University, School of Computer Science, 1979.Google Scholar
- [Michalski and Stepp, 1983]R. S. Michalski and R. E. Stepp. Learning from observation: Conceptual clustering. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, San Mateo, CA, 1983.Google Scholar
- [Minton, 1985]S. Minton. Selectively generalizing plans for problem-solving. In Proceedings of the 9th IJCAI, pages 596–599, Morgan Kaufmann, Los Angeles, CA, 1985.Google Scholar
- [Minton, 1988]S. Minton. Quantitative results concerning the utility of explanation-based learning. In Proceedings of the 7th AAAI, pages 564–569, Morgan Kaufmann, Seattle, WA, 1988.Google Scholar
- [Mitchell et al., 1986]T. M. Mitchell, R. M. Keller, and S. T. Kedar-Cabelli. Explanation-based generalization: A unifying view. Machine Learning, 1(1):47–80, 1986.Google Scholar
- [Mitchell et al.]T. M. Mitchell, J. Allen, P. Chalasani, J. Cheng, O. Etzioni, M. Ringuette, and J. C. Schlimmer. Theo: A framework for self-improving systems. In K. VanLehn, editor, Architectures for Intelligence, Earlbaum, Hillsdale, NJ.Google Scholar
- [Utgoff, 1986]
- [Wogulis and Langley, 1989]J. Wogulis and P. Langley. Improving efficiency by learning intermediate concepts. In Proceedings of the 8th AAAI, Morgan Kaufmann, Detroit, MI, 1989.Google Scholar