Argumentation is a formalism allowing to reason with contradictory information by modeling arguments and their interactions. There are now an increasing number of gradual semantics and impact measures that have emerged to facilitate the interpretation of their outcomes. An impact measure assesses, for each argument, the impact of other arguments on its score. In this paper, we refine an existing impact measure from Delobelle and Villata and introduce a new impact measure rooted in Shapley values. We introduce several principles to evaluate those two impact measures w.r.t. some well-known gradual semantics. This comprehensive analysis provides deeper insights into their functionality and desirability.

翻译：论辩是一种通过建模论证及其相互作用来处理矛盾信息的形式化推理方法。近年来，为便于解释论辩结果，涌现出越来越多的渐进语义与影响度量方法。影响度量旨在评估每个论证的得分受其他论证影响的程度。本文改进了Delobelle与Villata提出的现有影响度量，并引入一种基于Shapley值的新影响度量方法。我们提出了若干准则，用于在多种经典渐进语义框架下评估这两种影响度量。此项综合分析为深入理解其功能特性与适用性提供了新的见解。