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Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real‐World Problem‐Solving Task
Author(s) -
Ram Ashwin,
Narayanan S.,
Cox Michael T.
Publication year - 1995
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog1903_2
Subject(s) - troubleshooting , computer science , artificial intelligence , task (project management) , machine learning , multi task learning , instance based learning , semantic reasoner , active learning (machine learning) , engineering , systems engineering , operating system
This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real‐world troubleshooting tasks. 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 not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning.