Learning to Refine Indexing by
Introspective Reasoning*

Susan Fox and David B. Leake

Computer Science Department
Indiana University
Bloomington, IN 47405, USA



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.
References

1.	D. Aha, editor. Pmceedings of the AAAI-94 Workshop on Case-Based Reasoning, Seattle,
WA, July 1994.
2.	R. Alterman. An adaptive planner. In Proceedings of the Fifth National Conference on Artificial 
Intelligence, pages 6569, Philadelphia, PA, August 1986. AAAI.
3.	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.
4.	W.M. Bain. Case-based Reasoning: A Computer Model of Subjective Assessment. PhD thesis, 
Yale University, 1986. Computer Science Department Technical Report 470.
5.	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.
6.	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 215224, San Mateo, 1991.
DARPA, Morgan Kaufmann, Inc.
7.	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.
8.	R. J. Firby. Adaptive Execution in Complex Dynamic Worlds. PhD thesis, Yale University,
Computer Science Department, 1989. Technical Report 672.
9.	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 324329, Atlanta, GA, 1994. Lawrence Erlbaum Associates.
10.	S. Fox and D. Leake. An introspective reasoning method for index refinement. In Proceedings 
of 14th international Joint Conference on Artificial Intelligence. IJCAI, 1995.
11.	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).
12.	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.
13.	A. Goel, K. Ali, and Andrs Gmez de Silva Garza. Computational tradeoffs in experience-based 
reasoning. In Proceedings of the AAAI-94 workshop on Case-Based Reasoning, pages
5561, Seattle, WA, 1994.
14.	C. Hammond. Case-Based Planning: Viewing Planning as a Memory Task. Academic Press,
San Diego, 1989.
15.	J. Kolodner. Case-Based Reasoning. Morgan Kaufman, San Mateo, CA, 1993.
16.	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
313318, Bloomington, IN, 1992. Cognitive Science Society.
17.	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 675680. Lawrence Erlbaum Associates, 1994.
18.	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.
19.	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 209220, Berlin, 1994. Springer Verlag.
20.	E. Stroulia and A. Goel. Task structures: What to learn? In M. desJardins and A. Ram, edi-
tors, Proceedings of the 1994 AAAI Spring Symposium on Goal-driven Learning, pages 112121. AAAI Press, 1994.
