Abstract
Case-based problem-solving systems reason and learn from experiences, building up case libraries of problems and solutions to guide future reasoning. The expected benefits of this learning process depend on two types of regularity: (1) problem-solution regularity, the relationship between problem-to-problem and solution-to-solution similarity measures that assures that solutions to similar prior problems are a useful starting point for solving similar current problems, and (2) problem-distribution regularity, the relationship between old and new problems that assures that the case library will contain cases similar to the new problems it encounters. Unfortunately, these types of regularity are not assured. Even in contexts for which initial regularity is sufficient, problems may arise if a system’s users, tasks, or external environment change over time. This paper defines criteria for assessing the two types of regularity, discusses how the definitions may be used to assess the need for case-base maintenance, and suggests maintenance approaches for responding to those needs. In particular, it discusses the role of analysis of performance over time in responding to environmental changes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
W. Cheetham and J. Graf. Case-based reasoning in color matching. In Proceedings of the Second International Conference on Case-Based Reasoning, pages 1–12, Berlin, 1997. Springer Verlag.
Boi Faltings. Probabilistic indexing for case-based prediction. In Proceedings of the Second International Conference on Case-Based Reasoning, pages 611–622, Berlin, 1997. Springer Verlag.
S. Fox and D. Leake. Using introspective reasoning to refine indexing. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 391–397, San Francisco, CA, August 1995. Morgan Kaufmann.
S. Fox. Introspective Reasoning for Case-based Planning. PhD thesis, Indiana University, 1995. Computer Science Department.
M. Harries, K. Horn, and C. Sammut. Learning in time ordered domains with hidden changes in context. In Papers from the AAAI 1998 Workshop on Predicting the Future: AI Approaches to Time-Series Problems, pages 29–33. AAAI, 1998.
J. Kolodner. Case-Based Reasoning. Morgan Kaufmann, San Mateo, CA, 1993.
M. Kriegsman and R. Barletta. Building a case-based help desk application. IEEE Expert, 8(6):18–26, December 1993.
T. Lane and C. Brodley. Approaches to online learning and concept drift for user identification in computer security. In Papers from the AAAI 1998 Workshop on Predicting the Future: AI Approaches to Time-Series Problems, pages 64–70. AAAI, 1998.
D. Leake and D. Wilson. Case-base maintenance: Dimensions and directions. In P. Cunningham, B. Smyth, and M. Keane, editors, Proceedings of the Fourth European Workshop on Case-Based Reasoning, pages 196–207, Berlin, 1998. Springer Verlag.
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.
M. Salganicoff. Tolerating concept and sampling shift in lazy learning using prediction error context switching. Artificial Intelligence Review, 11(1-5):133–155, 1997.
B. Smyth and M. Keane. Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 377–382, Montreal, August 1995. IJCAI.
B. Smyth and M. Keane. FrDesign à la Déjà Vu: Reducing the adaptation overhead. In D. Leake, editor, Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press, Menlo Park, CA, 1996.
B. Smyth and E. McKenna. Modelling the competence of case-bases. In P. Cunningham, B. Smyth, and M. Keane, editors, Proceedings of the Fourth European Workshop on Case-Based Reasoning, pages 208–220, Berlin, 1998. Springer Verlag.
Houman Talebzadeh, Sanda Mandutianu, and Christian Winner. Countrywide loan-underwriting expert system. AI Magazine, 16(1):51–64, 1995.
M. Veloso. Planning and Learning by Analogical Reasoning. Springer Verlag, Berlin, 1994.
Ian Watson. Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Francisco, 1997.
J. Zhu and Q. Yang. Remembering to add: Competence-preserving case-addition policies for case based reasoning. 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leake, D.B., Wilson, D.C. (1999). When Experience is Wrong: Examining CBR for Changing Tasks and Environments⋆. In: Althoff, KD., Bergmann, R., Branting, L. (eds) Case-Based Reasoning Research and Development. ICCBR 1999. Lecture Notes in Computer Science, vol 1650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48508-2_16
Download citation
DOI: https://doi.org/10.1007/3-540-48508-2_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66237-2
Online ISBN: 978-3-540-48508-7
eBook Packages: Springer Book Archive