Order-of-Magnitude Influence Diagrams

Radu Marinescu; Nic Wilson

Order-of-Magnitude Influence Diagrams

Radu Marinescu, Nic Wilson

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:555-562, 2011.

Abstract

In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams.

Cite this Paper

BibTeX

@InProceedings{pmlr-vR9-marinescu11a,
  title = 	 {Order-of-Magnitude Influence Diagrams},
  author =       {Marinescu, Radu and Wilson, Nic},
  booktitle = 	 {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {555--562},
  year = 	 {2011},
  editor = 	 {Cozman, Fabio and Pfeffer, Avi},
  volume = 	 {R9},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {14--17 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/r9/main/assets/marinescu11a/marinescu11a.pdf},
  url = 	 {https://proceedings.mlr.press/r9/marinescu11a.html},
  abstract = 	 {In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams.},
  note =         {Reissued by PMLR on 04 October 2026.}
}

Endnote

%0 Conference Paper
%T Order-of-Magnitude Influence Diagrams
%A Radu Marinescu
%A Nic Wilson
%B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence
%C Proceedings of Machine Learning Research
%D 2011
%E Fabio Cozman
%E Avi Pfeffer	
%F pmlr-vR9-marinescu11a
%I PMLR
%P 555--562
%U https://proceedings.mlr.press/r9/marinescu11a.html
%V R9
%X In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams.
%Z Reissued by PMLR on 04 October 2026.

APA

Marinescu, R. & Wilson, N.. (2011). Order-of-Magnitude Influence Diagrams. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:555-562 Available from https://proceedings.mlr.press/r9/marinescu11a.html. Reissued by PMLR on 04 October 2026.

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