Quantitatively explaining the strength of arguments under gradual semantics has recently received increasing attention. Specifically, several works in the literature provide quantitative explanations by computing the attribution scores of arguments. These works disregard the importance of attacks and supports, even though they play an essential role when explaining arguments' strength. In this paper, we propose a novel theory of Relation Attribution Explanations (RAEs), adapting Shapley values from game theory to offer fine-grained insights into the role of attacks and supports in quantitative bipolar argumentation towards obtaining the arguments' strength. We show that RAEs satisfy several desirable properties. We also propose a probabilistic algorithm to approximate RAEs efficiently. Finally, we show the application value of RAEs in fraud detection and large language models case studies.

中文翻译：

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解释论据的强度：揭示攻击和支持的作用（技术报告）

在渐进语义下定量解释论证的强度最近受到越来越多的关注。具体来说，文献中的一些著作通过计算论点的归因分数来提供定量解释。这些著作忽视了攻击和支持的重要性，尽管它们在解释论证的强度时发挥着至关重要的作用。在本文中，我们提出了一种新的关系归因解释（RAE）理论，采用博弈论中的沙普利值来提供对攻击作用的细粒度见解，并支持定量双极论证以获得论证的强度。我们证明 RAE 满足几个理想的特性。我们还提出了一种概率算法来有效地近似 RAE。最后，我们展示了 RAE 在欺诈检测和大型语言模型案例研究中的应用价值。