Supporting Flexibility. a Case-based Reasoning Approach

This paper presents a case-based reasoning system TA3. We address the exibility of the case-based reasoning process, namely exible retrieval of relevant experiences, by using a novel similarity assessment theory. To exemplify the advantages of such an approach, we have experimentally evaluated the system and compared its performance to the performance of non-exible version of TA3 and to other machine learning algorithms on several domains.

[1]  Premkumar T. Devanbu,et al.  LaSSIE: a knowledge-based software information system , 1990, [1990] Proceedings. 12th International Conference on Software Engineering.

[2]  Peter Haddawy,et al.  Representations for Decision-Theoretic Planning: Utility Functions for Deadline Goals , 1992, KR.

[3]  A. Ortony,et al.  Similarity and Analogical Reasoning , 1991 .

[4]  Jon Doyle,et al.  Modular utility representation for decision-theoretic planning , 1992 .

[5]  Igor Jurisica.  Inductive Learning and Case-Based Reasoning , 1996 .

[6]  Igor Jurisica,et al.  A Similarity-Based Retrieval Tool for Software Repositories , 1995 .

[7]  Tibor Kökény,et al.  Constraint Satisfaction Problems with Order-Sorted Domains , 1995, Int. J. Artif. Intell. Tools.

[8]  Terry Gaasterland,et al.  Restricting query relaxation through user constraints , 1993, [1993] Proceedings International Conference on Intelligent and Cooperative Information Systems.

[9]  Eric Horvitz,et al.  Time-Dependent Utility and Action Under Uncertainty , 1991, UAI.

[10]  Matthias Jarke,et al.  Telos: representing knowledge about information systems , 1990, TOIS.

[11]  Bruce D'Ambrosio.  Process, structure and modularity in reasoning with uncertainty , 1988, UAI.

[12]  Vipul Kashyap,et al.  Schema Correspondences between Objects with Semantic Proximity , 1993 .

[13]  Daniel L. Chester,et al.  A Formal Basis for Analogical Reasoning , 1991, KR.

[14]  Julio Ortega,et al.  On the Informativeness of the DNA Promoter Sequences Domain Theory , 1994, J. Artif. Intell. Res..

[15]  Peter Haddawy,et al.  Anytime Deduction for Probabilistic Logic , 1994, Artif. Intell..

[16]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[17]  Igor Jurisica,et al.  How to Retrieve Relevant Information , 1994 .