Graphical Models for Preference and Utility
Rattachement africain : ca, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing notions of independence for utility functions in a multi-attribute space, and suggest that these can be used to achieve similar advantages. Our new results concern conditional additive independence, which we show always has a perfect representation as separation in an undirected graph (a Markov network). Conditional additive independencies entail a particular functional for the utility function that is analogous to a product decomposition of a probability function, and confers analogous benefits. This functional form has been utilized in the Bayesian network and influence diagram literature, but generally without an explanation in terms of independence. The functional form yields a decomposition of the utility function that can greatly speed up expected utility calculations, particularly when the utility graph has a similar topology to the probabilistic network being used.
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Le contrôle bibliographique ouvert
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University of Waterloo pays non établi dans la noticeUniversité ou école supérieure
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Princeton University pays non établi dans la noticeUniversité ou école supérieure
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University of. Waterloo Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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NEC Research Institute (Princeton pays non établi dans la noticeStructure de recherche
University of Waterloo, Princeton University et Department of Computer Science — University of. Waterloo, avec 1 autre affiliation.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.