Preference Factoring for Stochastic Trees
提出将偏好分解引入随机树模型，允许模型中的偏好部分像决策树一样独立处理，并探讨了可更新状态效用下的偏好摘要分解及多属性效用分解。
Stochastic trees are extensions of decision trees that facilitate the modeling of temporal uncertainties. Their primary application has been to medical treatment decisions. It is often convenient to present stochastic trees in factored form, allowing loosely coupled pieces of the model to be formulated and presented separately. In this paper, we show how the notion of factoring can be extended as well to preference components of the stochastic model. We examine updateable-state utility, a flexible class of expected utility models that permit stochastic trees to be rolled back much in the manner of decision trees. We show that preference summaries for updateable-state utility can be factored out of the stochastic tree. In addition, we examine utility decompositions which can arise when factors in a stochastic tree are treated as attributes in a multiattribute utility function.