文章基本信息

标题：Weighted Regret-Based Likelihood: A New Approach to Describing Uncertainty
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作者：Joseph Y. Halpern
期刊名称：Journal of Artificial Intelligence Research
印刷版ISSN：1076-9757
出版年度：2015
卷号：54
页码：471-492
出版社：American Association of Artificial
摘要：Recently, Halpern and Leung suggested representing uncertainty by a set of weighted probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event E'. In this paper, a notion of comparative likelihood when uncertainty is represented by a set of weighted probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given.