Abstract
The utility problem occurs when the performance of learning systems degrade instead of improve when additional knowledge is added. In lazy learners this degradation is seen as the increasing time it takes to search through this additional knowledge, which for a sufficiently large case base will eventually outweigh any gains from having added the knowledge. The two primary approaches to handling the utility problem are through efficient indexing and by reducing the number of cases during case base maintenance. We show that for many types of practical case based reasoning systems, the encountered case base sizes do not cause retrieval efficiency to degrade to the extent that it becomes a problem. We also show how complicated case base maintenance solutions intended to address the utility problem can actually decrease the combined system efficiency.
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aha, D.W.: The omnipresence of case-based reasoning in science and application. Knowledge-Based Systems 11, 261–273 (1998)
Bergmann, R., Richter, M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-oriented matching: A new research direction for case-based reasoning. In: 9th German Workshop on Case-Based Reasoning. Shaker Verlag, Aachen (2001)
Chaudhry, A., Holder, L.B.: An empirical approach to solving the general utility problem in speedup learning. In: IEA/AIE 1994: 7th international conference on Industrial and engineering applications of artificial intelligence and expert systems, pp. 149–158. Gordon and Breach Science Publishers, Inc., Newark (1994)
Cox, M.T.: Multistrategy learning with introspective meta-explanations. In: Machine Learning: Ninth International Conference, pp. 123–128. Morgan Kaufmann, San Francisco (1992)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI 2004: 6th conference on Symposium on Operating Systems Design & Implementation, pp. 137–150. USENIX Association, Berkeley (2004)
Forbus, K.D., Gentner, D., Law, K.: MAC/FAC: A Model of Similarity-Based Retrieval. Cognitive Science 19(2), 141–205 (1995)
Fox, S., Leake, D.B.: Combining case-based planning and introspective reasoning. In: 6th Midwest Artificial Intelligence and Cognitive Science Society Conference, pp. 95–103 (1995)
Francis, A., Ram, A.: A comparative utility analysis of case-based reasoning and control-rule learning systems. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 138–150. Springer, Heidelberg (1995)
Francis, A.G., Ram, A.: The utility problem in case based reasoning (1993)
Houeland, T.G., Aamodt, A.: Towards an introspective architecture for meta-level reasoning in clinical decision support system. In: ICCBR 2009, 7th Workshop on CBR in the Health Sciences (2009)
Jarmulak, J., Craw, S., Rowe, R.: Genetic Algorithms to Optimise CBR Retrieval. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 136–147. Springer, Heidelberg (2000)
Koton, P.A.: A method for improving the efficiency of model-based reasoning systems. Applied Artificial Intelligence 3(2-3), 357–366 (1989)
Leake, D.B., Smyth, B., Wilson, D.C., Yang, Q.: Introduction to the special issue on maintaining case-based reasoning systems. Computational Intelligence 17(2), 193–195 (2001)
Leake, D.B., Wilson, D.C.: Categorizing case-base maintenance: Dimensions and directions. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 196–207. Springer, Heidelberg (1998)
de Mántaras, R.L., McSherry, D., Bridge, D.G., Leake, D.B., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M.T., Forbus, K.D., Keane, M.T., Aamodt, A., Watson, I.D.: Retrieval, reuse, revision and retention in case-based reasoning. Knowledge Eng. Review 20(3), 215–240 (2005)
Markovitch, S., Scott, P.D.: The role of forgetting in learning. In: Fifth International Conference on Machine Learning, pp. 459–465. Morgan Kaufmann, Ann Arbor (1988)
Minton, S.: Quantitative results concerning the utility of explanation-based learning. Artif. Intell. 42(2-3), 363–391 (1990)
Shokouhi, S.V., Aamodt, A., Skalle, P., Sørmo, F.: Determining root causes of drilling problems by combining cases and general knowledge. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS (LNAI), vol. 5650, pp. 509–523. Springer, Heidelberg (2009)
Smyth, B., Cunningham, P., Cunningham, P.: The utility problem analysed - a case-based reasoning perspective. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 392–399. Springer, Heidelberg (1996)
Smyth, B., Keane, M.T.: Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In: 13th International Joint Conference on Artificial Intelligence, pp. 377–382. Morgan Kaufmann, San Francisco (1995)
Smyth, B., McKenna, E.: Footprint-based retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 343–357. Springer, Heidelberg (1999)
Sørmo, F.: Real-time drilling performance improvement. Scandinavian Oil & Gas Magazine, (7/8) (2009)
Tambe, M., Newell, A., Rosenbloom, P.S.: The problem of expensive chunks and its solution by restricting expressiveness. Machine Learning 5, 299–348 (1990)
Watson, I.: A case study of maintenance of a commercially fielded case-based reasoning system. Computational Intelligence 17, 387–398 (2001)
Zhu, J., Yang, Q.: Remembering to add: Competence-preserving case-addition policies for case-base maintenance. In: IJCAI 1999: 16th international joint conference on Artifical intelligence, pp. 234–239. Morgan Kaufmann Publishers Inc., San Francisco (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Houeland, T.G., Aamodt, A. (2010). The Utility Problem for Lazy Learners - Towards a Non-eager Approach. In: Bichindaritz, I., Montani, S. (eds) Case-Based Reasoning. Research and Development. ICCBR 2010. Lecture Notes in Computer Science(), vol 6176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14274-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-14274-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14273-4
Online ISBN: 978-3-642-14274-1
eBook Packages: Computer ScienceComputer Science (R0)