Diagnostic reasoning based on structure and behavior
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Operations Research
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Utility-based control for computer vision
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Model-Based Influence Diagrams for Machine Vision
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental construction of decision models. A uniform value-driven method of decision model construction is proposed for the hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is formulated as a stochastic process and modeled using influence diagrams. Given observations, this method creates decision models in order to obtain the best actions sequentially for locating and repairing a fault at minimum cost. This method construct decision models incrementally, interleaving probe actions with model construction and evaluation. The method treats meta-level and base-level tasks uniformly. That is, the method takes a decision-theoretic look at the control of search in causal pathways and structural hierarchies.