Abstract
This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We argue that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation. Finally, we show how other approaches from the literature can be classified according to the developed framework.
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References
A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39–59, 1994.
R. Bergmann. Effizientes Problemlösen durch flexible Wiederverwendung von Fällen auf verschiedenen Abstraktionsebenen. PhD thesis, University of Kaiserslautern, 1996.
R. Bergmann, G. Pews, and W. Wilke. Explanation-based similarity: A unifying approach for integrating domain knowledge into case-based reasoning. In S. Wess, K.-D. Althoff, and M.M. Richter, editors, Topics in Case-Based Reasoning, volume 837 of Lecture Notes on Artificial Intelligence, pages 182–196. Springer, 1994.
R. Bergmann and W. Wilke. Building and refining abstract planning cases by change of representation language. Journal of Artificial Intelligence Research, 3:53–118, 1995.
K. Branting and D. Aha. Stratified case-based reasoning: Reusing hierarchical problem solving episodes. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 384–390, 1995.
P. Cunningham, D. Finn, and S. Slattery. Knowledge engineering requirements in derivational analogy. volume 1 of LNAI, pages 234–245. Springer Verlag, 1994.
R. E. Fikes and N. J. Nilsson. Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189–208, 1971.
A. G. Francis and A. Ram. The utility problem in case-based reasoning. In Proceedings AAAI-93 Case-Based Reasoning Workshop, 1993.
F. Giunchiglia and T. Walsh. A theory of abstraction. Artificial Intelligence, 57:323–389, 1992.
K. Hanney, M. Keane, B. Smyth, and P. Cunningham. Systems, tasks and adaptation knowledge: Revealing some revealing dependencies. In M. Veloso and A. Aamodt, editors, Case-based Reasoning Research and Development, volume 1010 of Lecture Notes in AI, pages 461–470, 1995.
R. C. Holte, T. Mkadmi, R. M. Zimmer, and A. J. MacDonald. Speeding up problem solving by abstraction: A graph-oriented approach. Technical report, University of Ottawa, Ontario, Canada, 1995.
S. Kambhampati and J. Hendler. A validation-structure-based theory of plan modifications. Artificial Intelligence, 1992.
J. L. Kolodner. Case-Based Reasoning. Morgan Kaufmann, 1993.
M. Minsky. Steps toward artificial intelligence. In E. Feigenbaum, editor, Computers and Thought. McGraw-Hill, New York, NY, 1963.
T. M. Mitchell, R. M. Keller, and S. T. Kedar-Cabelli. Explanation-based generalization: A unifying view. Machine Learning, 1(1):47–80, 1986.
Gerd Pews and Stefan Wess. Combining case-based and model-based approaches for diagnostic applications in technical domains. In Proceedings EWCBR93, volume 2, pages 325–328, 1993.
E.D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115–135, 1974.
R. C. Schank. Dynamic Memory: A Theory of Learning in Computers and People. Cambridge University Press, New York, 1982.
B. Smyth and P. Cunningham. Deja vu: A hierarchical case-based reasoning system for software design. In ECAI-92, pages 587–589, 1992.
B. Smyth and M. Keane. Retrieving adaptable cases. In S. Wess, K.-D. Althoff, and M. M. Richter, editors, Topics in Case-Based Reasoning, pages 209–220. Springer, 1994.
B. Smyth and M. Keane. Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In Chris S. Mellish, editor, Proceedings of the International Conference on Artificial Intelligence, pages 377–383. Morgan Kaufmann Publishers, 1995.
M. Tambe and A. Newell. Some chunks are expensive. In Proceedings of the 5th International Conference on Machine Learning, pages 451–458, 1988.
M. Veloso, H. Munioz, and R. Bergmann. Case-based planning: Selected methods and systems. AI Communications, 1996. (in press).
A. Voss. Exploiting previous solutions — made easy. ftp://ftp.gmd.de//GMD/airesearch/Publications/Fabel/Prev-sol-voss.ps.gz, 1995.
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Bergmann, R., Wilke, W. (1996). On the role of abstraction in case-based reasoning. In: Smith, I., Faltings, B. (eds) Advances in Case-Based Reasoning. EWCBR 1996. Lecture Notes in Computer Science, vol 1168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020600
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DOI: https://doi.org/10.1007/BFb0020600
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