Order-of-magnitude influence diagrams

Radu Marinescu; Nic Wilson

Order-of-magnitude influence diagrams

Radu Marinescu
, Nic Wilson

IBM

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

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 orderof-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.

Original language	English
Title of host publication	Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
Publisher	AUAI Press
Pages	489-496
Number of pages	8
Publication status	Published - 2011

Publication series

Name	Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Cite this

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title = "Order-of-magnitude influence diagrams",

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 orderof-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.",

author = "Radu Marinescu and Nic Wilson",

year = "2011",

language = "English",

series = "Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011",

publisher = "AUAI Press",

pages = "489--496",

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Order-of-magnitude influence diagrams. / Marinescu, Radu; Wilson, Nic.
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011. AUAI Press, 2011. p. 489-496 (Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

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AB - 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 orderof-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.

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Order-of-magnitude influence diagrams

Abstract

Publication series

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