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Modeling diagnosis at multiple levels of abstraction. II. Diagnostic reasoning at multiple levels of abstraction
Author(s) -
Chu BeiTseng Bill,
Reggia James A.
Publication year - 1991
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550060604
Subject(s) - abstraction , inference , computer science , model based reasoning , domain (mathematical analysis) , representation (politics) , artificial intelligence , knowledge representation and reasoning , machine learning , causal inference , mechanism (biology) , theoretical computer science , mathematics , mathematical analysis , philosophy , epistemology , politics , political science , law , econometrics
Diagnostic reasoning at multiple levels of abstraction is an efficient problem‐solving strategy. It enables diagnostic problem‐solvers (human or automated) to efficiently form plausible high‐level diagnostic hypotheses while avoiding the explicit consideration of unnecessary details. This article describes a domain‐independent inference mechanism for diagnostic reasoning at multiple levels of abstraction. the inference mechanism uses the causal knowledge representation framework described in an earlier companion article. 1 This inference strategy has been tested through the implementation of a prototype diagnostic system with encouraging results.