Title:
Learning to troubleshoot: Multistrategy learning of diagnostic knowledge for a real-word problem solving task
Learning to troubleshoot: Multistrategy learning of diagnostic knowledge for a real-word problem solving task
Author(s)
Ram, Ashwin
Narayanan, Sundaram
Cox, Michael Thomas
Narayanan, Sundaram
Cox, Michael Thomas
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Abstract
This article presents a computational model of the
learning of diagnostic knowledge based on observations of human operators
engaged in a real-world troubleshooting task. We present a model of problem
solving and learning in which the reasoner introspects about its own
performance on the problem solving task, identifies what it needs to learn
to improve its performance, formulates learning goals to acquire the
required knowledge, and pursues its learning goals using multiple learning
strategies. The model is implemented in a computer system which provides a
case study based on observations of troubleshooting operators and protocol
analysis of the data gathered in the test area of an operational electronics
manufacturing plant. The model is intended as a computational model of
human learning; in addition, it is computationally justified as a uniform,
extensible framework for multi-strategy learning.
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Date Issued
1993
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286978 bytes
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Text
Resource Subtype
Technical Report