Troubleshooting using probabilistic networks and value of information Original Research Article

We develop a decision-theoretic method that yields approximate, low cost troubleshooting plans by making more relevant observations and devoting more time to generate a plan. The method is tested against other methods on three different problems in an experimental setting. Sensitivity analysis of the parameters is also carried out. The method yields low cost troubleshooting plans by spending substantially more computation time. The method turns out to be robust with respect to changes in observation and repair costs.