A methodology for incompleteness-tolerant and modular gradual semantics for argumentative statement graphs
OA Location
Author(s)
Rago, Antonio
Vasileiou, Stylianos Loukas
Toni, Francesca
Son, Tran Cao
Yeoh, William
Type
preprint
Abstract
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining GS for statement graphs, a form of structured argumentation framework, where arguments and relations between them are built from logical statements. Our methodology differs from existing approaches in the literature in two main ways. First, it naturally accommodates incomplete information, so that arguments with partially specified premises can play a meaningful role in the evaluation. Second, it is modularly defined to leverage on any GS for QBAFs. We also define a set of novel properties for our GS and study their suitability alongside a set of existing properties (adapted to our setting) for two instantiations of our GS, demonstrating their advantages over existing approaches
Date Issued
2024-10-29
Citation
arXiv, 2024
Journal / Book Title
arXiv
Copyright Statement
© 2024 The Author(s). This preprint is is made available under a CC-BY 4.0 International license (https://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
http://arxiv.org/abs/2410.22209v6
Subjects
cs.AI
cs.AI