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[2602.14674] From User Preferences to Base Score Extraction Functions in Gradual Argumentation
Summary
This paper introduces Base Score Extraction Functions in gradual argumentation, enhancing decision-making and AI transparency by mapping user preferences to argument scores.
Why It Matters
As AI systems become more prevalent, ensuring their transparency and accountability is crucial. This research provides a method to improve decision-making processes by aligning user preferences with argumentation frameworks, which can enhance the reliability of AI outputs in various applications.
Key Takeaways
- Introduces Base Score Extraction Functions for gradual argumentation.
- Enhances decision-making by mapping user preferences to argument scores.
- Proposes a method for approximating non-linear human preferences.
- Evaluates the approach theoretically and experimentally in robotics.
- Offers recommendations for selecting gradual semantics in practice.
Computer Science > Artificial Intelligence arXiv:2602.14674 (cs) [Submitted on 16 Feb 2026] Title:From User Preferences to Base Score Extraction Functions in Gradual Argumentation Authors:Aniol Civit, Antonio Rago, Antonio Andriella, Guillem Alenyà, Francesca Toni View a PDF of the paper titled From User Preferences to Base Score Extraction Functions in Gradual Argumentation, by Aniol Civit and 4 other authors View PDF HTML (experimental) Abstract: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 func...
