Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Readings in model-based diagnosis
Readings in model-based diagnosis
Decision-theoretic troubleshooting
Communications of the ACM
A comparison of decision alaysis and expert rules for sequential diagnosis
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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The goal of model-based diagnosis is to isolate causes of anomalous system behavior and recommend cost-effective repair actions. In general, precomputing optimal repair policies is intractable. To date, investigators addressing this problem have explored approximations that either impose restrictions on the system model, such as a single fault assumption, or that compute an immediate best action with limited lookahead. In this paper, we develop a formulation of repair in model-based diagnosis and a repair algorithm that computes optimal sequences of actions. This optimal approach is costly but can be applied to precompute an optimal repair strategy for compact systems. We show how we can exploit a hierarchical system specification to make this approach tractable for large systems. When introducing hierarchy, we also consider the tradeoff between simply replacing a component and decomposing it to repair its subcomponents. The hierarchical repair algorithm is suitable for off-line precomputation of an optimal repair strategy. A modification of the algorithm takes advantage of an iterative deepening scheme to trade off inference time and the quality of the computed strategy.