Valuation-based systems for Bayesian decision analysis
Operations Research
Structuring conditional relationships in influence diagrams
Operations Research
A Comparison of Graphical Techniques for Asymmetric Decision Problems
Management Science
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Welldefined decision scenarios
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
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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This paper deals with the representation and solution of asymmetric Bayesian decision problems. We present a formal framework, termed asymmetric influence diagrams, that is based on the influence diagram and allows an efficient representation of asymmetric decision problems. As opposed to existing frameworks, the asymmetric influence diagram primarily encodes asymmetry at the qualitative level and it can therefore be read directly from the model. We give an algorithm for solving asymmetric influence diagrams. The algorithm initially decomposes the asymmetric decision problem into a structure of symmetric subproblems organized as a tree. A solution to the decision problem can then be found by propagating from the leaves towards the root using existing evaluation methods to solve the subproblems.