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- W2283865319 abstract "This research outlines the design and implementation of the Adaptive Heuristic Reasoner, a diagnostic expert system based upon declarative associational knowledge which is capable of dynamically altering its strategic approach while pursuing a diagnostic result. Objectives have been to form a framework which would be perceived as logical, reasonable and understandable, and to make the system directly usable by domain experts by maximizing their ability to use familiar concepts and terms without requiring them to learn new syntax.Conceptually, the Reasoner represents a deliberate combination of numerical techniques, used solely for representing degrees of belief via belief functions, and symbolic techniques which are used for overall control purposes. The belief-function-based inference engine is constructed by converting an associative network of evidence items and hypotheses into a Markovian propagation tree. Belief functions, representing associational knowledge, are stored in the internal nodes of the tree and combined with belief functions representing the evidential data. Inferencing is accomplished by updating the belief functions at the hypothesis nodes as a result of the evidential data's propagation.Prospective control, which focuses upon the course of the diagnostic process as well as the diagnostic outcome, is symbolically facilitated by state analysis and strategy execution components. Using a clustering technique to partition the set of active hypotheses, the state of the diagnostic process is analyzed on each cycle in order to select the strategy which is most appropriate. Strategy execution focuses upon the selection of the best item of evidence, where each generic strategy uses different criteria for evaluating the evidence set.Primary contributions include: the coupling of a symbolic diagnostic control mechanism to a numerical belief-function-based inference engine; the explicit separation of the diagnostic control mechanism into components which assess the current diagnostic state and implement appropriate generic strategic actions; a bit vector representation for belief function value sets; a thresholding concept for belief function combining; a clustering methodology for partitioning the active hypothesis set; and a technique for selecting evidence which is maximally discriminatory." @default.
- W2283865319 created "2016-06-24" @default.
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- W2283865319 date "1990-01-01" @default.
- W2283865319 modified "2023-09-27" @default.
- W2283865319 title "Adaptive heuristic reasoning for belief-function-based diagnosis" @default.
- W2283865319 hasPublicationYear "1990" @default.
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