From Influence Diagrams to Multi-Operator Cluster DAGs
Abstract
There exist several architectures to solve influence diagrams using local computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation architectures. They all extend usual variable elimination algorithms thanks to the use of so-called 'potentials'. In this paper, we introduce a new architecture, called the Multi-operator Cluster DAG architecture, which can produce decompositions with an improved constrained induced-width, and therefore induce potentially exponential gains. Its principle is to benefit from the composite nature of influence diagrams, instead of using uniform potentials, in order to better analyze the problem structure.
Cite
Text
Pralet et al. "From Influence Diagrams to Multi-Operator Cluster DAGs." Conference on Uncertainty in Artificial Intelligence, 2006.Markdown
[Pralet et al. "From Influence Diagrams to Multi-Operator Cluster DAGs." Conference on Uncertainty in Artificial Intelligence, 2006.](https://mlanthology.org/uai/2006/pralet2006uai-influence/)BibTeX
@inproceedings{pralet2006uai-influence,
title = {{From Influence Diagrams to Multi-Operator Cluster DAGs}},
author = {Pralet, Cédric and Schiex, Thomas and Verfaillie, Gérard},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2006},
url = {https://mlanthology.org/uai/2006/pralet2006uai-influence/}
}