# From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)

arXiv: https://arxiv.org/abs/2602.14674 · HTML: https://mlsanity.com/paper/2602.14674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)](https://mlsanity.com/paper/2012.05738/) (2020)
- [Argumentative Large Language Models for Explainable and Contestable Claim Verification](https://mlsanity.com/paper/2405.02079/) (2024)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [A General Account of Argumentation with Preferences](https://mlsanity.com/paper/1804.06763/) (2018)
- [Value alignment: a formal approach](https://mlsanity.com/paper/2110.09240/) (2021)
- [SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report]](https://mlsanity.com/paper/2301.09559/) (2023)
- [When is it Better to Compare than to Score?](https://mlsanity.com/paper/1406.6618/) (2014)
- [On the Acceptability of Arguments in Preference-Based Argumentation](https://mlsanity.com/paper/1301.7358/) (2013)
- [Deep Argumentative Explanations](https://mlsanity.com/paper/2012.05766/) (2020)
- [Change in Quantitative Bipolar Argumentation: Sufficient, Necessary, and Counterfactual Explanations](https://mlsanity.com/paper/2509.18215/) (2025)
- [CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)](https://mlsanity.com/paper/2407.08497/) (2024)
- [On Gradual Semantics for Assumption-Based Argumentation](https://mlsanity.com/paper/2507.10076/) (2025)
- [Contestability in Quantitative Argumentation](https://mlsanity.com/paper/2507.11323/) (2025)

## Built on by (most cited first, 1 of 1)

- [Argumentation for Common Ground: Finding Zones of Possible Agreement between Individuals in Conflict](https://mlsanity.com/paper/2608.15634/) (2026)

## Abstract (arXiv)

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 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 Bipolar Argumentation Framework (BAF), supplemented with preferences, to obtain a 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.