Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It is considered a useful tool in domains such as decision-making, recommendation, debate analysis, and others. The outcomes in such domains are usually dependent on the arguments' base scores, which must be selected carefully. Often, this selection process requires user expertise and may not always be straightforward. On the other hand, organising the arguments by preference could simplify the task. In this work, we introduce \emph{Base Score Extraction Functions}, which provide a mapping from users' preferences over arguments to base scores. These functions can be applied to the arguments of a \emph{Bipolar Argumentation Framework} (BAF), supplemented with preferences, to obtain a \emph{Quantitative Bipolar Argumentation Framework} (QBAF), allowing the use of well-established computational tools in gradual argumentation. We outline the desirable properties of base score extraction functions, discuss some design choices, and provide an algorithm for base score extraction. Our method incorporates an approximation of non-linearities in human preferences to allow for better approximation of the real ones. Finally, we evaluate our approach both theoretically and experimentally in a robotics setting, and offer recommendations for selecting appropriate gradual semantics in practice.

翻译：渐进论证是符号人工智能的一个领域，因其能够支持透明且可争议的人工智能系统而受到关注。它被认为是决策制定、推荐、辩论分析等领域的有效工具。这些领域的结果通常依赖于论证的基础分数，因此必须谨慎选择这些分数。通常，这一选择过程需要用户具备专业知识，且并非总是直截了当。另一方面，通过偏好组织论证可以简化这一任务。在本研究中，我们引入了**基础分数提取函数**，这些函数提供了从用户对论证的偏好到基础分数的映射。这些函数可应用于带有偏好的**双极论证框架**（BAF），以获得**定量双极论证框架**（QBAF），从而允许使用渐进论证中成熟的计算工具。我们概述了基础分数提取函数的理想特性，讨论了一些设计选择，并提供了基础分数提取的算法。我们的方法融合了对人类偏好中非线性特征的近似，以更好地逼近真实偏好。最后，我们在机器人学环境中从理论和实验两方面评估了我们的方法，并为实践中选择合适的渐进语义提供了建议。