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

TL;DR

This paper introduces Base Score Extraction Functions that map user preferences to argument base scores in gradual argumentation, enhancing transparency and computational applicability in AI decision-making.

Contribution

It proposes a novel method to derive base scores from user preferences, including an algorithm and theoretical evaluation, facilitating better integration of human input in argumentation frameworks.

Findings

The method effectively approximates human preferences in argument scoring.

The approach enables the use of established computational tools in gradual argumentation.

Experimental evaluation demonstrates practical applicability in robotics settings.

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.