Abstract
A significant problem for case-based reasoning (CBR) systems is determining the features to use in judging case similarity for retrieval. We describe research that addresses the feature selection problem by using introspective reasoning to learn new features for indexing. Our method augments the CBR system with an introspective reasoning component which monitors system performance to detect poor retrievals, identifies features which would lead retrieval of more adaptable cases, and refines the indexing criteria to include the needed features to avoid future failures. We explore the benefit of introspective reasoning by performing empirical tests on the implemented system. These tests examine the effect of introspective index refinement, and the effects of problem order on case and index learning, and show that introspective learning of new index features improves performance across the different problem orders.
This work was supported in part by the National Science Foundation under Grant No. IRI-9409348.
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References
D. Aha, editor. Proceedings of the AAAI-94 Workshop on Case-Based Reasoning, Seattle, WA, July 1994.
R. Alterman. An adaptive planner. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 65–69, Philadelphia, PA, August 1986. AAAI.
J. Arcos and E. Plaza. A reflective architecture for integrated memory-based learning and reasoning. In S. Wess, K.D. Altoff, and M. Richter, editors, Topics in Case-Based Reasoning. Springer-Verlag, Kaiserslautern, Germany, 1993.
W.M. Bain. Case-based Reasoning: A Computer Model of Subjective Assessment. PhD thesis, Yale University, 1986. Computer Science Department Technical Report 470.
S. Bhatta and A. Goel. Model-based learning of structural indices to design cases. In Proceedings of the IJCAI-93 Workshop on Reuse of Design, Chambery, France, September 1993. IJCAI.
L. Birnbaum, G. Collins, M. Brand, M. Freed, B. Krulwich, and L. Pryor. A model-based approach to the construction of adaptive case-based planning systems. In R. Bareiss, editor, Proceedings of the Case-Based Reasoning Workshop, pages 215–224, San Mateo, 1991. DARPA, Morgan Kaufmann, Inc.
M. Cox and A. Ram. Managing learning goals in strategy-selection problems. In Proceedings of the Second European Workshop on Case-Based Reasoning, pages 85–93, Chantilly, France, 1994.
R. J. Firby. Adaptive Execution in Complex Dynamic Worlds. PhD thesis, Yale University, Computer Science Department, 1989. Technical Report 672.
S. Fox and D. Leake. Using introspective reasoning to guide index refinement in case-based reasoning. In Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, pages 324–329, Atlanta, GA, 1994. Lawrence Erlbaum Associates.
S. Fox and D. Leake. An introspective reasoning method for index refinement. In Proceedings of 14th international Joint Conference on Artificial Intelligence. IJCAI, 1995.
S. Fox and D. Leake. Modeling case-based planning for repairing reasoning failures. In Proceedings of the 1995 AAAI Spring Symposium on Representing Mental States and Mechanisms, Stanford, CA, March 1995. AAAI. (ftp.cs.indiana.edu:/pub/leake/p-95-02.ps.Z).
M. Freed and G. Collins. Adapting routines to improve task coordination. In Proceedings of the 1994 Conference on AI Planning Systems, pages 255–259, 1994.
A. Goel, K. Ali, and Andrés Gómez de Silva Garza. Computational tradeoffs in experience-based reasoning. In Proceedings of the AAAI-94 workshop on Case-Based Reasoning, pages 55–61, Seattle, WA, 1994.
C. Hammond. Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, San Diego, 1989.
J. Kolodner. Case-Based Reasoning. Morgan Kaufman, San Mateo, CA, 1993.
D. Leake. Constructive similarity assessment: Using stored cases to define new situations. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pages 313–318, Bloomington, IN, 1992. Cognitive Science Society.
R. Oehlmann, P. Edwards, and D. Sleeman. Changing the viewpoint: Re-indexing by introspective questioning. In Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, pages 675–680. Lawrence Erlbaum Associates, 1994.
M. Redmond. Learning by Observing and Understanding Expert Problem Solving. PhD thesis, College of Computing, Georgia Institute of Technology, 1992. Technical report GIT-CC-92/43.
B. Smyth and M. Keane. Retrieving adaptable cases: The role of adaptation knowledge in case retrieval. In S. Wess, K. Althoff, and M Richter, editors, Topics in Case-Based Reasoning, pages 209–220, Berlin, 1994. Springer Verlag.
E. Stroulia and A. Goel. Task structures: What to learn? In M. desJardins and A. Ram, editors, Proceedings of the 1994 AAAI Spring Symposium on Goal-driven Learning, pages 112–121. AAAI Press, 1994.
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Fox, S., Leake, D.B. (1995). Learning to refine indexing by introspective reasoning. In: Veloso, M., Aamodt, A. (eds) Case-Based Reasoning Research and Development. ICCBR 1995. Lecture Notes in Computer Science, vol 1010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60598-3_39
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DOI: https://doi.org/10.1007/3-540-60598-3_39
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