Probabilistic decision graphs for optimization under uncertainty

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Abstract

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions.

The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.
Original languageEnglish
Journal4OR
Volume9
Issue number1
Pages (from-to)1-28
Number of pages28
ISSN1619-4500
DOIs
Publication statusPublished - 2011

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Influence Diagrams
Decision problem
Uncertainty
Optimization
Graph in graph theory
Alternatives
Modeling
Software System
Approximate Solution
Graph
Demonstrate
Framework
Influence diagrams

Cite this

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Probabilistic decision graphs for optimization under uncertainty. / Jensen, Finn V.; Nielsen, Thomas Dyhre.

In: 4OR, Vol. 9, No. 1, 2011, p. 1-28.

Research output: Contribution to journalJournal articleResearchpeer-review

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