By Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun Preece (eds.)

The papers accrued during this quantity have been provided on the sixth eu C- ference on Case-Based Reasoning (ECCBR 2002) held on the Robert Gordon collage in Aberdeen, united kingdom. This convention a sequence of very succe- ful well-established biennial eu workshops held in Trento, Italy (2000), Dublin, eire (1998), Lausanne, Switzerland (1996), and Paris, France (1994), after the preliminary workshop in Kaiserslautern, Germany (1993). those conferences have a background of attracting ?rst-class eu and foreign researchers and practitioners within the years interleaving with the biennial foreign co- terpart ICCBR; the 4th ICCBR convention used to be held in Vancouver, Canada in 2001. complaints of ECCBR and ICCBR meetings are ordinarily released by means of Springer-Verlag of their LNAI sequence. Case-Based Reasoning (CBR) is an AI problem-solving method the place pr- lems are solved by means of retrieving and reusing recommendations from comparable, formerly solved difficulties, and doubtless revising the retrieved approach to re?ect di?erences - tween the hot and retrieved difficulties. Case wisdom shops the formerly solved difficulties and is the most wisdom resource of a CBR process. a primary concentration of CBR examine is the illustration, acquisition and upkeep of case wisdom. lately different wisdom resources were famous as very important: indexing, similarity and variation wisdom. Signi?cant wisdom engine- ing e?ort will be wanted for those, and so the illustration, acquisition and upkeep of CBR wisdom extra often became important.

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Extra info for Advances in Case-Based Reasoning: 6th European Conference, ECCBR 2002 Aberdeen, Scotland, UK, September 4–7, 2002 Proceedings

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2 Similarity Knowledge for Parameter Values The use of similarities between finding values can improve learning methods and reasoning capabilities dramatically. For example, the construction of diagnostic profiles benefits from similarity knowledge, because similar values of a parameter can work together in a diagnostic profile, rather than to compete against each other. Thus we consider learning similarities before building diagnostic profiles. Inductive Learning for Case-Based Diagnosis with Multiple Faults 33 In the following we will use the term distance function, but it is obvious, that a distance function d directly corresponds to a similarity function sim.

When (not) to Use Derivational Analogy: Lessons Learned Using APU. : Proceeding of AAAI-94 Workshop: Casebased Reasoning. (1994) 9. : Analysis and Empirical Studies of Derivational Analogy. Artificial Intelligence 67 (1994) 287–328 10. : Modelling of Engineering Thermal Problems - An implementation using CBR with Derivational Analogy. In: Proceedings of EWCBR’93, Springer-Verlag (1993) 11. : Derivation Replay for Partial-Order Planning. AAAI1994 (1994) 12. : Planning for Manufacturing Workpieces by Storing, Indexing and Replaying Planning Decisions.

Of the International Conference on Machine Learning, pages 194–202, 1995. 13. Dan Ventura and Tony R. Martinez. An Empirical Comparison of Discretization Methods. In Proc. of the 10th Int. Symp. on Computer and Information Sciences, pages 443–450, 1995. 14. Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Mateo, California, 2000. 15. Craig Stanfill and David Waltz. Toward Memory-Based Reasoning. Communications of the ACM, 29(12):1213–1228, 1986.

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