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The Explanatory Power of Symbolic Similarity in Case-Based Reasoning

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Abstract

A desired capability of automatic problem solvers is that they can explain the results. Such explanations should justify that the solution proposed by the problem solver arises from the known domain knowledge. In this paper we discuss how explanations can be used in case-based reasoning (CBR) in order to justify the results in classification tasks and also for solving new problems. We particularly focus on explanations derived from building a symbolic description of the similar aspects among cases. Moreover, we show how symbolic descriptions of similarity can be exploited in the different processes of CBR, namely retrieve, reuse, revise, and retain.

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References

  • A. Aamodt E. Plaza (1994) ArticleTitleCase-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches Artificial Intelligence Communications 7 IssueID1 39–59

    Google Scholar 

  • E. Armengol E. Plaza (2001a) Lazy Induction of Descriptions for Relational Case-Based Learning L. Raedt Particlede P. Flach (Eds) Machine Learning: ECML-2001, vol. 2167 of Lecture Notes in Artificial Intelligence Springer Verlag Berlin 13–24

    Google Scholar 

  • E. Armengol E. Plaza (2001b) Similarity Assessment for Relational CBR D.W. Aha I. Watson (Eds) Case-Based Reasoning Research and Development. ICCBR-2001. vol. 2080 of Lecture Notes in Artificial Intelligence Springer Verlag Berlin 44–58

    Google Scholar 

  • E. Armengol E. Plaza (2003) Remembering Similitude Terms in Case-Based Reasoning P. Perner A. Rosenfeld (Eds) Machine Learning and Data Mining: MLDM-03, vol. 2734 of Lecture Notes in Artificial Intelligence Springer Verlag Berlin 121–130

    Google Scholar 

  • Börner, K. (1993). Structural Similarity as Guidance in Case-Based Design. In Wess, S., Althoff, K. D. & Richter, M. M. (eds.) Proc. of the First European Workshop on Case-Based Reasoning, vol. 1, 14–19. University of Kaiserslautern.

  • Bunke, H. & Messmer, B. (1993). Similarity Measures for Structured Representations. In Wess, S., Althoff, K. D. & Richter, M. M. (eds.) Topics in Case-Based Reasoning, vol. 837 of Lecture Notes in Computer Science, 106–118.

  • D. Doyle P. Cunningham P. Bridge Y. Rahman (2004) Explanation-Oriented Retrieval P. A. González-Calero P. Funk (Eds) Advances in Case-Based Reasoning, vol. 3155 of LNAI Springer Verlag Berlin 157–168

    Google Scholar 

  • Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann.

  • Leake, D. (1994). Issues in Goal-Driven Explanation. In Proceeding of the AAAI Spring Symposium on Goal-Driven Learning, 72–79. AAAI Press: Portland.

  • D. Leake (1995) ArticleTitleAbduction, Experience and Goals: A Model of Everyday Abductive Explanation Journal of Experimental and Theoretical Artificial Intelligence 7 407–428

    Google Scholar 

  • R. López de Mántaras (1991) ArticleTitleA Distance-Based Attribute Selection Measure for Decision Tree Induction Machine Learning 6 81–92 Occurrence Handle10.1023/A:1022694001379

    Article  Google Scholar 

  • McSherry, D. (2005). Explanation in Recommender Systems. Artificial Intelligence Review. This Issue.

  • Nugent, C. & Cunningham, P. (2005). A Case-Based Explanation System for Black-Box Systems. Artificial Intelligence Review. This Issue.

  • Ontañón, S. & Plaza, E. (2003). Justification-Based Multiagent Learning. In Fawcett, T. & Mishra, N. (eds.) In Proceedings 20th International Conference on Machine Learning, 576–583. Morgan Kaufmann.

  • E. Plaza (1995) Cases as Terms: A Feature Term Approach to the Structured Representation of Cases M. Veloso A. Aamodt (Eds) Case-Based Reasoning Research and Development. ICCBR-95, vol. 1010 of Lecture Notes in Artificial Intelligence Springer Verlag Berlin 265–276

    Google Scholar 

  • Sørmo, F., Cassens, J. & Aamodt, A. (2005). Explanation in Case-Based Reasoning: Perspectives and Goals. Artificial Intelligence Review. This Issue.

  • W. Swartout J. Moore (1993) Explanation in Second Generation Expert Systems J. M. David J. P. Krivine R. Simmons (Eds) Second Generation Expert Systems Springer Verlag Berlin 543–585

    Google Scholar 

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Authors and Affiliations

  1. IIIA Artificial Intelligence Research Institute, CSIC Spanish Council for Scientific Research, Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza, Eva Armengol & Santiago Ontañón

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  1. Enric Plaza
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  2. Eva Armengol
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  3. Santiago Ontañón
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Correspondence to Enric Plaza.

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Plaza, E., Armengol, E. & Ontañón, S. The Explanatory Power of Symbolic Similarity in Case-Based Reasoning. Artif Intell Rev 24, 145–161 (2005). https://doi.org/10.1007/s10462-005-4608-6

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  • DOI: https://doi.org/10.1007/s10462-005-4608-6

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