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Hierarchical model-based diagnosis for high autonomy systems

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Abstract

Deep reasoning diagnostic procedures are model-based, inferring single or multiple faults from the knowledge of faulty behavior of component models and their causal structure. The overall goal of this paper is to develop a hierarchical diagnostic system that exploit knowledge of structure and behavior. To do this, we use a hierarchical architecture including local and global diagnosers. Such a diagnostic system for high autonomy systems has been implemented and tested on several examples in the domain of robot-managed fluid-handling laboratory.

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Authors and Affiliations

  1. Department of Computer Science, Hankuk Aviation University, Seoul, Korea

    Sung-Do Chi

  2. AI-Simulation Group, Electrical and Computer Engineering, University of Arizona, 85721, Tucson, AZ, USA

    Bernard P. Zeigler

Authors
  1. Sung-Do Chi
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  2. Bernard P. Zeigler
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Additional information

Research supported by NASA-Ames Co-operative Agreement No. NCC 2-525, ‘A Simulation Environment for Laboratory Management by Robot Organization’.

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Chi, SD., Zeigler, B.P. Hierarchical model-based diagnosis for high autonomy systems. J Intell Robot Syst 9, 193–207 (1994). https://doi.org/10.1007/BF01276498

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  • DOI: https://doi.org/10.1007/BF01276498

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