Learning case relevance in case-based reasoning with abstract argumentation
File(s)
Published version
Author(s)
Paulino Passos, Guilherme
Toni, Francesca
Type
Conference Paper
Abstract
Case-based reasoning is known to play an important role in several legal settings. We focus on a recent approach to case-based reasoning, supported by an instantiation of abstract argumentation whereby arguments represent cases and attack between arguments results from outcome disagreement between cases and a notion of relevance. We explore how relevance can be learnt automatically with the help of decision trees, and explore the combination of case-based reasoning with abstract argumentation (AA-CBR) and learning of case relevance for prediction in legal settings. Specifically, we show that, for two legal datasets, AA-CBR with decision-tree-based learning of case relevance performs competitively in comparison with decision trees, and that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts, which could facilitate cognitively tractable explanations.
Date Issued
2023-12-01
Date Acceptance
2023-10-20
Citation
Frontiers in Artificial Intelligence and Applications, 2023, 379, pp.95-1000
ISSN
0922-6389
Publisher
IOS Press
Start Page
95
End Page
1000
Journal / Book Title
Frontiers in Artificial Intelligence and Applications
Volume
379
Copyright Statement
© 2023 The Authors.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
License URL
Source
36th International Conference on Legal Knowledge and Information Systems
Publication Status
Published
Start Date
2023-12-18
Finish Date
2023-12-20
Coverage Spatial
Maastricht, the Netherlands