Fuzzy Analogical Model of Adaptation for Case-Based Reasoning

Case-based reasoning is a recognized paradigm and has been explored in both applied and methodological directions. In the several phases of CBR, the adaptation phase is certainly the most problematic whereas the most characteristic and interesting phase. We propose to view this task through a fuzzy analogical scheme. The adaptation is realized by focusing on the relation existing between the problem to be solved and the retrieved cases. Two approaches are proposed here: the relation can be captured by a fuzzy linguistic modifier or by a fuzzy interpolation.

[1]  C. Marsala,et al.  Linguistic modifiers and measures of similarity or resemblance , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[2]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[3]  Rainer Schmidt,et al.  Case-based adaptation problems in medicine , 2003 .

[4]  C. Marsala,et al.  Interpolative reasoning based on graduality , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[5]  Marie-Jeanne Lesot,et al.  Similarity measures for binary and numerical data: a survey , 2008, Int. J. Knowl. Eng. Soft Data Paradigms.

[6]  B. Bounchon,et al.  Stability of linguistic modifiers compatible with a fuzzy logic , 1988 .

[7]  Henri Prade,et al.  Case-Based Reasoning: A Fuzzy Approach , 1997, Fuzzy Logic in Artificial Intelligence.

[8]  Francisco Herrera,et al.  Fuzzy Sets and Their Extensions: Representation, Aggregation and Models - Intelligent Systems from Decision Making to Data Mining, Web Intelligence and Computer Vision , 2007, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[9]  Ralph Bergmann,et al.  Towards a New Formal Model of Transformational Adaptation in Case-Based Reasoning , 1998, ECAI.

[10]  Alain Mille,et al.  A Knowledge-Level Task Model of Adaption in Case-Based Reasoning , 1999, ICCBR.

[11]  Bernadette Bouchon-Meunier,et al.  Linguistic modifiers and imprecise categories , 1992, Int. J. Intell. Syst..

[12]  B. Bouchon-Meunier,et al.  Interpolative reasoning with multi-variable rules , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[13]  C. Marsala,et al.  Several forms of fuzzy analogical reasoning , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[14]  Bernadette Bouchon-Meunier,et al.  Towards general measures of comparison of objects , 1996, Fuzzy Sets Syst..

[15]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[16]  Bernadette Bouchon-Meunier.  Fuzzy logic and knowledge representation using linguistic modifiers , 1992 .

[17]  Francisco Herrera,et al.  Fuzzy Sets and Their Extensions: Representation, Aggregation and Models , 2008 .

[18]  L. Godo,et al.  On similarity logic and the generalized modus ponens , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[19]  Jaime G. Carbonell,et al.  Derivational analogy: a theory of reconstructive problem solving and expertise acquisition , 1993 .

[20]  I. Burhan Türksen,et al.  An approximate analogical reasoning approach based on similarity measures , 1988, IEEE Trans. Syst. Man Cybern..

[21]  Piero P. Bonissone,et al.  SOFT-CBR: A Self-Optimizing Fuzzy Tool for Case-Based Reasoning , 2003, ICCBR.

[22]  Olga Vorobieva,et al.  Prototypes for Medical Case-Based Applications , 2008, ICDM.

[23]  Bernadette Bouchon-Meunier,et al.  Fuzzy Prototypes: From a Cognitive View to a Machine Learning Principle , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[24]  A. Tversky.  Features of Similarity , 1977 .

[25]  Bernadette Bouchon-Meunier,et al.  Stability of Linguistic Modifiers Compatible with a Fuzzy Logic , 1988, IPMU.