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 college in Aberdeen, united kingdom. This convention a sequence of very succe- ful well-established biennial ecu 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 historical past of attracting ?rst-class ecu and overseas researchers and practitioners within the years interleaving with the biennial overseas co- terpart ICCBR; the 4th ICCBR convention used to be held in Vancouver, Canada in 2001. lawsuits of ECCBR and ICCBR meetings are commonly released through Springer-Verlag of their LNAI sequence. Case-Based Reasoning (CBR) is an AI problem-solving procedure the place pr- lems are solved via retrieving and reusing ideas from comparable, formerly solved difficulties, and probably revising the retrieved option to re?ect di?erences - tween the recent and retrieved difficulties. Case wisdom shops the formerly solved difficulties and is the most wisdom resource of a CBR procedure. a major concentration of CBR learn is the illustration, acquisition and upkeep of case wisdom. lately different wisdom resources were well-known as very important: indexing, similarity and version wisdom. Signi?cant wisdom engine- ing e?ort should be wanted for those, and so the illustration, acquisition and upkeep of CBR wisdom extra normally 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
9 abn(x)=A5 1 where abn(x) is a function returning the abnormality for the given value and A0 defines a normal value. , dp (x, y) = 1 for abn(x) = A5 and abn(y) = A0. 34 Joachim Baumeister, Martin Atzm¨uller, and Frank Puppe After that, we compute the similarities for the remaining values by applying the VDM method (see Equation 8) for the values contained in the “normal values”–partition and for the values contained in the “abnormal values”–partition. Scalability Knowledge. Beyond abnormalities the expert may mark some of the parameters as scaled to characterize, that values, that are closer to each other, are more similar.
For example, preprocessing heterogeneous data or learning parameter weights still needs improvements. Besides the diagnostic profile generation process, we are currently working on an enhanced approach taking advantage of causal independencies between groups of diagnoses. Furthermore the case base is still growing due to the routine usage of the system in a clinical environment. An evaluation of the presented methods with a larger number of cases should yield better results. References 1. Michael Richter.
In , Priar  and SPA  are cited as examples of systems performing this conservative adaptation strategy. Interestingly, neither of these systems performs plan adaptation by Derivational Analogy; cases in these systems contains plans and provably correct plan transformation rules are used to perform adaptation. So the question remains: does adaptation by derivational analogy perform a conservative plan modiﬁcation strategy in the sense of the MODSAT deﬁnition or not? If it does, then the worst case analysis applies and thus the empirical results reported about case-based planners such as Prodigy/Analogy, DerSNLP and CAPlan/CbC outperforming their respective underlying ﬁrst-principles planners would have been mere accidents.
Advances in Case-Based Reasoning: 6th European Conference, ECCBR 2002 Aberdeen, Scotland, UK, September 4–7, 2002 Proceedings by Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun Preece (eds.)