Probabilistic decision graphs for optimization under uncertainty
Publication: Research - peer-review › Journal article
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Probabilistic decision graphs for optimization under uncertainty. / Jensen, Finn V.; Nielsen, Thomas Dyhre.
In: 4 O R, Vol. 9, No. 1, 2011, p. 1-28.Publication: Research - peer-review › Journal article
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TY - JOUR
T1 - Probabilistic decision graphs for optimization under uncertainty
A1 - Jensen,Finn V.
A1 - Nielsen,Thomas Dyhre
AU - Jensen,Finn V.
AU - Nielsen,Thomas Dyhre
PB - Springer
PY - 2011
Y1 - 2011
N2 - 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.<br/><br/>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.
AB - 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.<br/><br/>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.
U2 - 10.1007/s10288-011-0159-7
DO - 10.1007/s10288-011-0159-7
JO - 4 O R
JF - 4 O R
SN - 1619-4500
IS - 1
VL - 9
SP - 1
EP - 28
ER -