Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
A Decision-Analytic Model for Using Scientific Data
UAI '89 Proceedings of the Fifth 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
Problem formulation as the reduction of a decision model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Refinement and coarsening of Bayesian networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Reflection and action under scarce resources: theoretical principles and empirical study
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A logic and time nets for probabilistic inference
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
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A new probabilistic network construction system, DYNASTY, is proposed for diagnostic reasoning given variables whose probabilities change over time. Diagnostic reasoning is formulated as a sequential stochastic process, and is modeled using influence diagrams. Given a set O of observations, DYNASTY creates an influence diagram in order to devise the best action given O. Sensitivity analyses are conducted to determine if the best network has been created, given the uncertainty in network parameters and topology. DYNASTY uses an equivalence class approach to provide decision thresholds for the sensitivity analysis. This equivalence-class approach to diagnostic reasoning differentiates diagnoses only if the required actions are different. A set of network-topology updating algorithms are proposed for dynamically updating the network when necessary.