Abstract
We can learn a lot about what features are important for retrieval by comparing similar cases in a case-base. We can determine which features are important in predicting outcomes and we can assign weights to features accordingly. In the same manner we can discover which features are important in specific contexts and determine localised feature weights that are specific to individual cases. In this paper we describe a comprehensive set of techniques for learning local feature weights and we evaluate these techniques on a case-base for conflict resolution in air traffic control. We show how introspective learning of feature weights improves retrieval and how it can be used to determine context sensitive local weights. We also show that introspective learning does not work well in case-bases containing only pivotal cases because there is no redundancy to be exploited.
This research is funded by Eurocontrol Experimental Centre, Bretigny-sur-Orge, France.
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References
Birnbaum, L., Collins, G., Brand, M., Freed, M., Krulwich, B., and Prior, L. (1991) A Model-Based Approach to the Construction of Adaptive Case-Based Planning Systems. Proceedings of the Case-Based Reasoning Workshop, pp. 215–224. Washington D.C., USA.
Bonzano A., & Cuningham P., (1996) ISAC: A CBR System for Decision support in Air Traffic Control in Proceedings of EWCBR '96, Advances in Case-Based Reasoning, Ian Smith & Boi Faltings eds. Springer Verlag Lecture Notes in AI, pp44–57.
Fox, S. & Leake, D. B. (1995) Using Introspective Reasoning to Refine Indexing. Proceedings of the 14 th International Joint Conference on Artificial Intelligence, pp. 391–397.
Laird, J. E, Rosenbloom, P. S., and Newell, A. (1986) Chucking in Soar: The Anatomy of a General Learning Mechanism. Machine Learning. 1(1).
Laird, J. E., Newell, A., and Rosenbloom, P. S. (1987) Soar: An Architecture for General Intelligence. Artificial Intelligence. 33(1).
Leake, D. B., Kinley, A., and Wilson, D. (1995) Learning to Improve Case Adaptation by Introspective Reasoning and CBR. Case-Based Reasoning Research and Development (Ed.s M. Veloso & A. Aamodt), Proceedings of the 1th International Conference on Case-Based Reasoning, pp. 229–240, Springer-Verlag.
Munoz-Avila, H., Hüllen, J. (1996) Feature Weighting by ExplainingCase-Based Planning Episodes. Advances in Case-Based Reasoning (Ed.s I. Smith & B. Faltings), Proceedings of the Third European Workshop on Case-Based Reasoning, pp. 280–294, Springer-Verlag.
Oehlmann, R., Edwards, P., & Sleeman, D. (1995) Changing the Viewpoint: Re-Indexing by Introspective Question. Proceedings of the 16th Annual Conference of the Cognitive Science Society, pp. 381–386. Lawrence-Erlbaum and Associates.
Saltzburg, S. L. (1991) A Nearest Hyperrectangle Learning Method. Machine Learning, 1.
Stefik, M. (1981) Planning and Meta-Planning. Artificial Intelligence, 16, pp. 141–170.
Smyth, B. & Keane, M. T. (1995) Remembering to Forget: A Competence Preserving Case Deletion Policy for CBR Systems. Proceedings of the 14th International Joint Conference on artificial Intelligence (IJCAI-95), pp. 377–382. Montreal, Canada.
Veloso, M. (1992) Learning by Analogical Reasoning in General Problem Solving. Ph.D. Thesis, (CMU-CS-92-174), School of Computer Science, Carnegie Mellon University, Pittsburgh, USA.
Watson I. D., (1996) Case-Based Reasoning Tools: An Overview, In Proceedings of 2nd. UK CBR Workshop, Progress in Case-Based Reasoning, Watson. I.D. (Ed.) pp.71–88. University of Salford. (also available on the Web at http://146.87.176.38/ai-cbr/Papers/cbrtools.doc)
Wetterschereck, D., & Aha, D. W. (1995) Weighting Features. Case-Based Reasoning Research and Development (Ed.s M. Veloso & A. Aamodt), Proceedings of The 1st International Conference on Case-Based Reasoning, pp. 347–358, Springer-Verlag.
Wettschereck, D., Aha, D. W., & Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. To appear in Artificial Intelligence Review. (also available on the Web from http://www.aic.nrl.navy.mil/~aha/)
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Bonzano, A., Cunningham, P., Smyth, B. (1997). Using introspective learning to improve retrieval in CBR: A case study in air traffic control. In: Leake, D.B., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 1997. Lecture Notes in Computer Science, vol 1266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63233-6_500
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DOI: https://doi.org/10.1007/3-540-63233-6_500
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