Abstract
This paper establishes a formal model for hybrid diagnosis, novel features including: (1) It provides a unified theoretical framework for utilizing device models and heuristics in diagnosis, which naturally integrates all the important components of diagnosis — the structural and behavioral description of devices, fault modes, the lower and upper fault bounds, fault possibilities and heuristic rules — into a diagnostic system. Device models predict outputs from inputs, heuristic rules infer the possibilities of certain components being faulty from symptoms, and yet the combination of both constrains each other for us to reduce the hypothesis space. (2) It presents a typical way of modeling behavior of devices, to which the key is the introduction of I-O functions with indefinite inputs/outputs. (3) It can easily be implemented over a forward-chaining inference engine.
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Project partly supported by the National Natural Science Foundation of China.
Shen Yidong is a Professor of computer science at Chongqing, University. He received his Ph.D. degree in computer science from Chongqing University in 1991. He was a visiting researcher at the University of Valenciennes, France (1992–1993), the University of Maryland Institute for Advanced Computer Studies, USA (1995–1996) and the University of Alberta, Canada (1998–1999), respectively. His research interests include artificial intelligence, deductive and object-oriented databases, logic programming and parallel processing.
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Shen, Y. A theory of hybrid diagnosis. J. Comput. Sci. & Technol. 14, 363–371 (1999). https://doi.org/10.1007/BF02948738
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DOI: https://doi.org/10.1007/BF02948738