Knowledge representation and inference in intelligent decision systems
Knowledge representation and inference in intelligent decision systems
Probabilistic inference and influence diagrams
Operations Research
Intelligent decision systems
Representation requirements for supporting decision model formulation
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Dynamic network updating techniques for diagnostic reasoning
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Integrating model construction and evaluation
UAI '92 Proceedings of the eighth 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
The concept of influence and its use in structuring complex decision problems.
The concept of influence and its use in structuring complex decision problems.
Constructing situation specific belief networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We propose an approach to knowledge-based decision-model construction. The domain knowledge is represented in the form of multilevel influence diagrams. At each level, a regular influence diagram representing a decision model may be generated and the influence diagrams between the levels are related through a series of operations. We defined eight such operations and show that these are sufficient at the structural level to construct any target influence diagram in any practical situation. We provide a series of examples drawn from different domains on the use of multilevel influence diagrams for model construction. We also propose suggestions on providing users with guidance that can direct the model-construction procedure towards the best target model.