Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Modelling Challenges with Influence Diagrams: Constructing Probability and Utility Models
Año:2010
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
DECISION SUPPORT SYSTEMS
ISSN
0167-9236
Factor de impacto JCR
2,622
Información de impacto
Volumen
49
DOI
10.1016/j.dss.2010.04.003
Número de revista
4
Desde la página
354
Hasta la página
364
Mes
NOVIEMBRE
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Participantes
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Participante: M. Gómez (Universidad de Granada)
  • Participante: P.P. Shenoy (Universidad de Kansas)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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