Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Structuring conditional relationships in influence diagrams
Operations Research
A Computational Theory of Decision Networks
A Computational Theory of Decision Networks
A New Method for Influence Diagram Evaluation
A New Method for Influence Diagram Evaluation
Decision graphs: algorithms and applications to influence diagram evaluation and high-level path planning under uncertainty
Sequential influence diagrams: A unified asymmetry framework
International Journal of Approximate Reasoning
Evaluating asymmetric decision problems with binary constraint trees
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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While influence diagrams have many advantages as a representation framework for Bayesian decision problems, they have a serious drawback in handling asymmetric decision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A considerable amount of unnecessary computation may be involved when a symmetrized influence diagram is evaluated by conventional algorithms. In this paper we present an approach for avoiding such unnecessary computation in influence diagram evaluation.