Operations Research
Valuation-based systems for Bayesian decision analysis
Operations Research
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Evaluating Influence Diagrams using LIMIDs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this paper we present three different architectures for the evaluation of influence diagrams: HUGIN, Shafer-Shenoy (S-S), and Lazy Propagation (LP). HUGIN and LP are two new architectures introduced in this paper. The computational complexity using the three architectures are compared on the same structure, the Limited Memory Influence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depicted. Because the requisite information is explicitly represented in the diagram, the evaluation procedure can take advantage of it. Previously, it has been shown that significant savings in computational time can be obtained by performing the calculation on the LIMID rather than on the traditional influence diagram. In this paper we show how the obtained savings is considerably increased when the computations are performed according to the LP scheme.