TI Learning to Troubleshoot: Multistrategy Learning of Diagnostic
Knowledge for a Real-World Problem Solving Task 

AU Ashwin Ram, S. Narayanan, Michael T. Cox 

PU Cognitive Science journal, 19(3):289-340, 1995 

PU Technical Report GIT-CC-9367, College of Computing, Georgia
Institute of Technology, Atlanta, GA, 1993 

AV ftp:ftp.cc.gatech.edupubairamgit-cc-93-67.ps.Z 

YR 1993 

AB 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
multistrategy learning.