Abstract
Gradual semantics 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 gradual semantics for structured argumentation frameworks, where the building blocks of arguments and relations between them are known, unlike in QBAFs, where arguments are abstract entities. Differently from existing approaches, our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology, leveraging existing gradual semantics for QBAFs in these more complex frameworks. We also define a set of novel properties for gradual semantics in structured argumentation, discuss their suitability over a set of existing properties. Finally, we provide a comprehensive theoretical analysis assessing the instantiations, demonstrating the their advantages over existing gradual semantics for QBAFs and structured argumentation.
Abstract (translated)
逐步语义在论证中展现出了巨大的潜力，特别是在部署定量双极论证框架（QBAF）于各种现实世界场景中的应用，从判断性预测到可解释的人工智能。本文提供了一种新颖的方法论来获取结构化论证框架的逐步语义，在这种框架下，论证的构建块及其之间的关系是已知的，这与QBAFs不同，在后者中论证被视为抽象实体。不同于现有的方法，我们的方法可以处理关于前提信息不完整的情况。我们通过引入该方法的两种不同实例来展示其潜力，这些实例利用了现有针对QBAF的逐步语义在更复杂框架中的应用。我们还定义了一组适用于结构化论证的新属性，并讨论了它们相对于已有属性的适用性。最后，我们提供了一个全面的理论分析来评估这些实例，展示了与现有QBAF和结构化论证的逐步语义相比的优势。
URL
https://arxiv.org/abs/2410.22209
