This paper presents a novel framework for structured argumentation, named extend argumentative decision graph ($xADG$). It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The $xADG$ framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structure, resulting in more concise argumentation graphs that may be easier for users to understand. The study presents a methodology for construction of $xADGs$ and evaluates their size and predictive capacity for classification tasks of varying magnitudes. Resulting $xADGs$ achieved strong (balanced) accuracy, which was accomplished through an input decision tree, while also reducing the average number of supports needed to reach a conclusion. The results further indicated that it is possible to construct plausibly understandable $xADGs$ that outperform other techniques for building $ADGs$ in terms of predictive capacity and overall size. In summary, the study suggests that $xADG$ represents a promising framework to developing more concise argumentative models that can be used for classification tasks and knowledge discovery, acquisition, and refinement.

翻译：本文提出了一种名为可扩展论证决策图（$xADG$）的新型结构化论证框架。该框架基于Dung的抽象论证图扩展了论证决策图，允许论证在其内部结构中使用布尔逻辑运算与多重前提（支持关系），从而生成更加简洁且更易于用户理解的论证图。研究阐述了$xADG$的构建方法，并评估了其在处理不同规模分类任务时的规模与预测能力。通过输入决策树，所构建的$xADG$在实现强（平衡）准确率的同时，减少了达成结论所需的平均支持数量。结果进一步表明，构建可被合理理解的$xADG$是可行的，其在预测能力和整体规模上优于其他构建$ADG$的技术。综上，本研究指出$xADG$是开发更简洁论证模型的有前景框架，可应用于分类任务及知识发现、获取与精炼。