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Title:Human Pattern Recognition and Information Seeking in Simulated Fault Diagnosis Tasks
Author(s):Hunt, Ruston McClellan
Department / Program:Mechanical Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Industrial
Computer Science
Abstract:An experiment was conducted in which thirty-four subjects solved a total of 5440 simulated fault diagnosis problems over a period of ten weeks. Two simulations were used; a context-free simulation known as TASK and a context-specific simulation known as FAULT. Results showed that context-free computer aiding helped to reduce the number of errors committed on the context-specific simulation.
A fuzzy rule-based model was developed to match human problem solving behavior. The model was able to match 50% of the subjects' actions exactly and it used the same rules approximately 70% of the time. Problem solving rules were selected by the model according to measures of recall, applicability, usefulness, and simplicity. Rules were further discriminated by their use of symptomatic information for pattern recognition (S-Rules) or topographic information for information seeking (T-Rules). The overall results are discussed with regards to the implications for fault diagnosis training.
Issue Date:1981
Description:101 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981.
Other Identifier(s):(UMI)AAI8203491
Date Available in IDEALS:2014-12-13
Date Deposited:1981

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