提出'特征'概念，让论证分析更精细高效。
Facets in Argumentation: A Formal Approach to Argument Significance
- 引入'特征'概念，定位部分但非全部扩展中的论点
- 特征相关任务复杂度远低于统计扩展数
- 适合需要筛选或理解论点重要性的用户
论证是人工智能中建模与推理论点的核心领域。抽象论证框架（AFs）的语义由论点集合（扩展）及其关系条件（如稳定或可接受）定义。当前求解器可完成寻找扩展、判断宽容或怀疑性接受、计数或枚举扩展等任务。尽管这些任务已明确，但在决策与枚举之间的细粒度推理仍需高成本计算。本文提出一种新概念——特征（facets），用于介于决策与枚举之间的推理。特征指属于某些扩展（宽容性）但不属于所有扩展（怀疑性）的论点，最适用于用户在导航、过滤或理解特定论点意义时的需求。我们研究其复杂性，发现涉及特征的任务比统计扩展数要简单得多。最后，我们实现了该方法并进行了实验，验证了其可行性。
原文摘要 · Abstract (English)
Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the relationship between them, such as stable or admissible. Today's solvers implement tasks such as finding extensions, deciding credulous or skeptical acceptance, counting, or enumerating extensions. While these tasks are well charted, the area between decision, counting/enumeration and fine-grained reasoning requires expensive reasoning so far. We introduce a novel concept (facets) for reasoning between decision and enumeration. Facets are arguments that belong to some extensions (credulous) but not to all extensions (skeptical). They are most natural when a user aims to navigate, filter, or comprehend the significance of specific arguments, according to their needs. We study the complexity and show that tasks involving facets are much easier than counting extensions. Finally, we provide an implementation, and conduct experiments to demonstrate feasibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。