Some abstract argumentation approaches consider that arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained from a abstract argumentation framework (AAF) under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknown and sometimes there is a need for aggregating the probability values of different sources. In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise. We use credal sets to model the uncertainty values of arguments and from these credal sets, we calculate the lower and upper bounds of the extensions. We study some properties of the suggested approach and illustrate it with an scenario of decision making.

中文翻译：

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一种基于信任集的抽象论证的不精确概率方法

一些抽象论证方法认为论证具有一定程度的不确定性，这会影响从语义下的抽象论证框架（AAF）获得的扩展的不确定性程度。在这些方法中，参数和扩展的不确定性都是通过精确的概率值来建模的。然而，在许多现实生活中，确切的概率值是未知的，有时需要汇总不同来源的概率值。在本文中，考虑到参数的概率值不精确，我们解决了计算扩展不确定度的问题。我们使用信任集来模拟参数的不确定性值，并从这些信任集计算扩展的下限和上限。