提出一种能处理不完整论证信息的模块化渐进语义方法
A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
- 基于逻辑语句构建论证图，支持部分前提缺失的论点参与评估
- 可灵活适配任意量化双边论证框架的渐进语义，保持计算模块化
- 新定义属性验证更优，适合需鲁棒性与可扩展性的解释型AI场景
渐进语义（GS）在论证系统中展现出巨大潜力，尤其适用于从判断预测到可解释AI等现实场景中的量化双边论证框架（QBAFs）。本文提出一种获取陈述图渐进语义的新方法，陈述图是一种由逻辑语句构建的结构化论证框架。该方法在两方面区别于现有研究：一是天然支持不完整信息，使前提部分指定的论点仍能有意义地参与评估；二是模块化定义，可利用任意现有QBAF的渐进语义。我们还定义了一组新属性，并对两种实例化方法在新旧属性上的表现进行研究，证明其优于现有方法。
原文摘要 · Abstract (English)
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining GS for statement graphs, a form of structured argumentation framework, where arguments and relations between them are built from logical statements. Our methodology differs from existing approaches in the literature in two main ways. First, it naturally accommodates incomplete information, so that arguments with partially specified premises can play a meaningful role in the evaluation. Second, it is modularly defined to leverage on any GS for QBAFs. We also define a set of novel properties for our GS and study their suitability alongside a set of existing properties (adapted to our setting) for two instantiations of our GS, demonstrating their advantages over existing approaches.
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