Integrating user preferences into gradual bipolar argumentation for personalised decision support
File(s)SUM2024_paper_7760.pdf (596.62 KB)
Accepted version
Author(s)
Battaglia, Elisa
Baroni, Pietro
Rago, Antonio
Toni, Francesca
Type
Conference Paper
Abstract
Gradual bipolar argumentation has been shown to be an
effective means for supporting decisions across a number of domains. Individual user preferences can be integrated into the domain knowledge represented by such argumentation frameworks and should be taken into account in order to provide personalised decision support. This however
requires the definition of a suitable method to handle user-provided preferences in gradual bipolar argumentation, which has not been considered in previous literature. Towards filling this gap, we develop a conceptual analysis on the role of preferences in argumentation and investigate some basic principles concerning the effects they should have on the evaluation of strength in gradual argumentation semantics. We illustrate an application of our approach in the context of a review aggregation system, which has been enhanced with the ability to produce personalised
outcomes based on user preferences.
effective means for supporting decisions across a number of domains. Individual user preferences can be integrated into the domain knowledge represented by such argumentation frameworks and should be taken into account in order to provide personalised decision support. This however
requires the definition of a suitable method to handle user-provided preferences in gradual bipolar argumentation, which has not been considered in previous literature. Towards filling this gap, we develop a conceptual analysis on the role of preferences in argumentation and investigate some basic principles concerning the effects they should have on the evaluation of strength in gradual argumentation semantics. We illustrate an application of our approach in the context of a review aggregation system, which has been enhanced with the ability to produce personalised
outcomes based on user preferences.
Date Issued
2024-11-12
Date Acceptance
2024-08-31
Citation
Lecture Notes in Artificial Intelligence, 2024, pp.14-28
ISSN
1611-3349
Publisher
Springer
Start Page
14
End Page
28
Journal / Book Title
Lecture Notes in Artificial Intelligence
Copyright Statement
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
Identifier
https://link.springer.com/chapter/10.1007/978-3-031-76235-2_2
Source
Scalable Uncertainty Management, 16th International Conference (SUM 2024)
Publication Status
Published
Start Date
2024-11-27
Finish Date
2024-11-29
Coverage Spatial
Palermo, Italy
Date Publish Online
2024-11-12