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Titlebook: Expert Systems in Engineering: Principles and Applications; Principles and Appli Georg Gottlob,Wolfgang Nejdl Conference proceedings 1990 S

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https://doi.org/10.1007/978-3-658-15416-5 algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct and complete, and it always returns a minimal plan if there is a plan at all.
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https://doi.org/10.1007/978-3-658-33657-8acterize the typically very large number of diagnoses, usually only the minimal such sets of failing components are represented. This method of characterizing all diagnoses is inadequate in general, in part because not every superset of the faulty components of a diagnosis necessarily provides a dia
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https://doi.org/10.1007/978-3-662-62379-4 deals with numerical values that change with time and manages inaccuracies in these values. This is achieved by working on data that are arrays of numerical intervals. Imprecision in models is also processed by means of numerical intervals..An ATMS extension which encompasses the management of inte
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https://doi.org/10.1007/978-3-642-47932-8ystem which allows the fast and effective realization of different measurement selection strategies for model-based diagnosis systems. The minimum entropy technique used in the General Diagnostic Engine (.) and a simplification of it presented in [de Kleer 89] are two possible instances of this syst
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https://doi.org/10.1007/978-3-658-42759-7 doing exoneration. A robust theory of diagnosis must not presume the ways components fail. In order to build both, a parsimonious and robust theory of diagnosis, all the proofs which lead to the conclusion that components are not abnormal must be defeasible. This is a basic motivation to apply non
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https://doi.org/10.1007/978-3-658-15416-5 algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct and complete, and it always returns a minimal plan if there is a plan at
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