From User Preferences to Base Score Extraction Functions in Gradual Argumentation

2026-02-16

AI Summary

This study addresses the problem of automatically extracting intrinsic weights from users’ preferences over arguments to construct interpretable gradual argumentation systems. It formally introduces, for the first time, a base weight extraction function that maps qualitative preferences to quantitative base weights for building Quantitative Bipolar Argumentation Frameworks (QBAFs). By incorporating the nonlinear characteristics of human preferences, the approach enables transparent and contestable AI decision-making. Integrating bipolar argumentation frameworks, a base weight extraction algorithm, and gradual semantics, the work provides both theoretical analysis and empirical validation through robotic experiments, demonstrating its effectiveness. Furthermore, it offers practical guidance for selecting appropriate gradual semantics in real-world applications.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 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.

Problem

• gradual argumentation
• base score
• user preferences
• argumentation framework
• preference elicitation

Innovation

• Base Score Extraction Functions
• Gradual Argumentation
• Bipolar Argumentation Framework
• Preference Modeling
• Quantitative Argumentation