论文标题
基于信用集的抽象论证的一种不精确的概率方法
An Imprecise Probability Approach for Abstract Argumentation based on Credal Sets
论文作者
论文摘要
一些抽象论证方法认为，论证具有一定程度的不确定性，这影响了从语义学下从抽象论证框架（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建模的。但是，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性程度的问题。我们使用信用集来建模参数的不确定性值，从这些信用集中，我们计算了扩展的下限和上限。我们研究了建议的方法的一些特性，并以决策的情况进行了说明。
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.
