Abstract
The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems which uses computational models of problem solving systems to isolate the root causes of a utility problem, to detect the threshold conditions under which the problem will arise, and to design strategies to eliminate it. We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem. Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Barret, A. & Weld, D. (1994). Partial order planning: evaluating possible efficiency gains. Artificial Intelligence, 67 (1), 71–112.
Etzioni, O. (1992) An Asymptotic Analysis of Speedup Learning. In Machine Learning: Proceedings of the 9th International Workshop, 1992.
Francis, A. and Ram, A. (1993). Computational Models of the Utility Problem and their Application to a Utility Analysis of Case-Based Reasoning. In Proceedings of the Third International Workshop on Knowledge Compilation and Speedup Learning, pages 48–55, University of Massachusetts at Amherst, June 30, 1993.
Gratch, J.M. and Dejong, G.F. (1991) Trouble with gestalts: The composability problem in control learning. Technical Report, University of Illinois at Urbana-Champaign, April 1991.
Hanks, S. & Weld, D.S. (1995). A domain-independent algorithm for plan adaptation. Journal of Artificial Intelligence Research 2 (1995), 319–360.
Holder, L.B.; Porter, B.W.; Mooney, R.J. (1990). The general utility problem in machine learning. In Machine Learning: Proceedings of the Seventh International Conference, 1990.
Ihrig, L. & Kambhampati, S. (1994). On the Relative Utility of Plan-Space v. State-Space Planning in a Case-Based Framework, Technical Report 94-006, Department of Computer Science and Engineering, 1994. Arizona State University.
Kolodner, J.L. & Simpson, R.L. (1988). The Mediator: A case study of a case-based reasoner. Georgia Institute of Technology, School of Information and Computer Science, Technical Report no. GIT-ICS-88/11. Atlanta, Georgia.
Kolodner, J.L. (1993). Case-based Reasoning. Morgan Kaufmann, 1993.
Koton, P. A. (1989). A Method for Improving the Efficiency of Model-Based Reasoning Systems. Laboratory for Computer Science, MIT, Cambridge, MA. Hemisphere Publishing, 1989.
Markovitch, S. and Scott, P.D. (1993) Information Filtering: Selection Methods in Learning Systems. Machine Learning, 10: 113–151.
Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In Proceedings of the Seventh National Conference on Artificial Intelligence, Morgan Kaufmann, 1988.
Minton, S. (1990). Quantitative results concerning the utility of explanation-based learning. Artificial Intelligence, 42(2–3), March 1990.
Newell, A. & Simon, H.A. (1975). Computer Science as Empirical Inquiry: Symbols and Search. Reprinted in Haugeland, J., (ed.). Mind Design: Philosophy, Psychology, Artificial Intelligence, chapter 1, pp 35–66. MIT Press, 1981.
Ram, A. & Hunter, L. (1992) The Use of Explicit Goals for Knowledge to Guide Inference and Learning. Applied Intelligence, 2(1):47–73.
Schank, R. & Abelson, R. (1977). Scripts, Plans, Goals and Understanding. Lawrence Erlbaum Associates, Hillsdale, NJ.
Simon, H.A. (1993). Artificial Intelligence as an Experimental Science. Invited Talk. Abstracted in Proceedings of the Eleventh National Conference on Artificial Intelligence, page 853, July 11–15, 1993.
Tambe, M.; Newell, A.; Rosenbloom, P. S. (1990). The Problem of Expensive Chunks and its Solution by Restricting Expressiveness. Machine Learning, 5:299–348, 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Francis, A.G., Ram, A. (1995). A comparative utility analysis of case-based reasoning and control-rule learning systems. In: Lavrac, N., Wrobel, S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science, vol 912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59286-5_54
Download citation
DOI: https://doi.org/10.1007/3-540-59286-5_54
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-59286-0
Online ISBN: 978-3-540-49232-0
eBook Packages: Springer Book Archive