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Decision theoretic troubleshooting combines Bayesian networks and cost estimates to obtain optimal or near optimal decisions in domains with inherent uncertainty. In this paper we use the well-known A* algorithm extended with pruning based on the efficiency of actions for finding optimal solutions in troubleshooting. In particular, we focus on models with dependent actions.
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