将归因解释用于复杂循环的真相发现框架，揭示可信度判断背后的深层逻辑。
Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks
- 在循环结构的真相发现框架中同时应用论点与关系归因解释。
- 两类归因方法均揭示出非直观且有洞察力的可信度分析结果。
- 适合对可信度推理机制感兴趣的研究者或系统设计者。
在渐进语义下解释论点强度正受到越来越多关注。现有研究通过计算定量双极论证框架（QBAFs）中论点或边的归因得分来提供解释，称为论点归因解释（AAE）和关系归因解释（RAE），常用移除法和Shapley值法。尽管这些方法在无环QBAFs中已证明有效，但在含复杂环路的场景中仍缺乏探索。此外，多数应用仅关注单一类型归因，未进行直接对比。本文首次将AAE与RAE应用于真相发现QBAFs（TD-QBAFs），该框架评估来源（如网站）及其主张（如病毒严重性）的可信度，具有复杂环路结构。实验表明，两类归因方法均可提供有趣且非平凡的解释，揭示出令人意外的深层见解。
原文摘要 · Abstract (English)
Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the attribution scores of arguments or edges in Quantitative Bipolar Argumentation Frameworks (QBAFs). These explanations, known as Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs), commonly employ removal-based and Shapley-based techniques for computing the attribution scores. While AAEs and RAEs have proven useful in several applications with acyclic QBAFs, they remain largely unexplored for cyclic QBAFs. Furthermore, existing applications tend to focus solely on either AAEs or RAEs, but do not compare them directly. In this paper, we apply both AAEs and RAEs, to Truth Discovery QBAFs (TD-QBAFs), which assess the trustworthiness of sources (e.g., websites) and their claims (e.g., the severity of a virus), and feature complex cycles. We find that both AAEs and RAEs can provide interesting explanations and can give non-trivial and surprising insights.
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