A methodology for incompleteness-tolerant and modular gradual semantics for argumentative statement graphs
File(s)
Accepted version
Author(s)
Rago, Antonio
Vasileiou, Stylianos Loukas
Tran, Son
Toni, Francesca
Yeoh, William
Type
Conference Paper
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
2025-11-11
Date Acceptance
2025-07-10
Citation
Proceedings of the 22nd International Conference on Principles of Knowledge Representation and Reasoning (KR 2025), 2025, pp.500-511
ISBN
978-1-956792-08-9
ISSN
2334-1033
Publisher
International Joint Conferences on Artificial Intelligence Organization
Start Page
500
End Page
511
Journal / Book Title
Proceedings of the 22nd International Conference on Principles of Knowledge Representation and Reasoning (KR 2025)
Copyright Statement
© 2025 International Joint Conferences on Artificial Intelligence Organization.
Source
22nd International Conference on Principles of Knowledge Representation and Reasoning (KR 2025)
Publication Status
Published
Start Date
2025-11-11
Finish Date
2025-11-17
Coverage Spatial
Melbourne, Australia