量化论证框架中不同结论集的差异程度，揭示观点分歧的大小。
Diversity of Extensions in Abstract Argumentation
- 用对称差定义扩展多样性，衡量不同结论集的差异
- 证明了计算最大多样性水平的复杂性分类结果
- 适合研究论证系统分歧性质或可解释性的研究人员
论证是人工智能中建模与推理论点的重要课题。在抽象论证中，我们考虑表达论点间冲突的有向图，即论证框架（AF）。语义通过‘扩展’定义，即满足框架中特定关系条件的论点集合。传统论证推理通常不揭示扩展之间的距离。本文基于对称差引入扩展多样性的定量概念，并提供系统的复杂性分类。直观上，多样性反映一个框架的扩展（被接受的观点）仅微小差异，还是代表根本冲突的论点集合。我们研究框架是否具有k-多样扩展、是否覆盖特定论点的k-多样扩展，以及计算最大可实现的k值。我们实现了一个原型并评估了多样性水平的计算性能。
原文摘要 · Abstract (English)
Argumentation is an important topic of AI for modelling and reasoning about arguments. In abstract argumentation, we consider directed graphs, so-called argumentation frameworks (AF), that express conflicts between arguments. The semantics is defined by the notion of extensions, which are sets of arguments that satisfy particular relationship conditions in the AF. Usually, standard reasoning in argumentation do not reveal how far apart extensions are. We introduce a quantitative notion of diversity of extensions based on the symmetric difference and provide a systematic complexity classification. Intuitively, diversity captures whether extensions of a framework (accepted viewpoints) differ only marginally or represent fundamentally incompatible sets of arguments. We study whether an AF admits k-diverse extensions, admits k-diverse extensions covering specific arguments, and to compute the largest k for which an AF admits k-diverse extensions. We outline a prototype and provide an evaluation for computing diversity levels.
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