Titlebook: Case-Based Reasoning; A Textbook Michael M. Richter,Rosina O. Weber Textbook 2013 Springer-Verlag GmbH Germany 2013 Artificial intelligence

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s of CBR without assuming prior reader knowledge; Part II explains the core methods, in particu-lar case representations, similarity topics, retrieval, adaptation, evaluation, revisions, learning, develop-ment,978-3-662-52377-3978-3-642-40167-1

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er summaries, background notes, and exercises throughout.Inc.While it is relatively easy to record billions of experiences in a database, the wisdom of a system is not measured by the number of its experiences but rather by its ability to make use of them. Case-based rea-soning (CBR) can be viewed a

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Uncertaintyer-controlled way. Fuzzy sets and their logical descriptions are introduced. Examples show the relations between fuzzy sets and CBR. In addition, possibility and necessity are introduced. Both have a close relation to CBR. Formalisms are introduced insofar as they are needed for the relations to CBR.

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Multiplexing optical fiber sensors, domains typical of CBR. We recommend it to readers who have completed reading the previous chapters. The descriptions are intended to provide examples for illustrating the concepts and methods mentioned mostly in Chap. ., Basic CBR Elements. Throughout the chapter, we reference where to read further about new topics.

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Optical Fiber Sensor Technologyglobal principle. This principle allows describing all further representations, such as object-oriented, hierarchical, taxonomies, and graph-oriented representations. Trees are used for compact representations of attribute-value vectors. Graph structures often underlie more general object-oriented representations.

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