期刊
DECISION SUPPORT SYSTEMS
卷 49, 期 4, 页码 354-364出版社
ELSEVIER
DOI: 10.1016/j.dss.2010.04.003
关键词
Decision-making under uncertainty; Influence diagrams; Bayesian networks; Probabilistic graphical models; Generalized additive independence networks; Ceteris paribus networks; Utility ceteris paribus networks; Expected utility networks; Utility diagrams
类别
资金
- Spanish Ministry of Education and Science [TIN2007-62626, TIN2007-67418-C03-03]
Influence diagrams have become a popular tool for representing and solving complex decision-making problems under uncertainty. In this paper, we focus on the task of building probability models from expert knowledge, and also on the challenging and less known task of constructing utility models in influence diagrams. Our goal is to review the state of the art and list some challenges. Similarly to probability models, which are embedded in influence diagrams as a Bayesian network, preferential/utility independence conditions can be used to factor the joint utility function into small factors and reduce the number of parameters needed to fully define the joint function. A number of graphical models have been recently proposed to factor the joint utility function, including the generalized additive independence networks, ceteris paribus networks, utility ceteris paribus networks, expected utility networks, and utility diagrams. Similarly to probability models, utility models can also be engineered from a domain expert or induced from data. (C) 2010 Elsevier B.V. All rights reserved.
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