On monotonicity of dispute trees as explanations for case-based reasoning with abstract argumentation
File(s)
Published version
Author(s)
Paulino-Passos, G
Toni, F
Type
Conference Paper
Abstract
Recent work on explainability raises the question of what different types of explanations actually mean. One idea is that explanations can reveal information about the behaviour of the model on a subset of the input space. When this way of interpreting explanations is thought as an interactive process, inferences from explanations can be seen as a form of reasoning. In the case of case-based reasoning with abstract argumentation (AA-CBR), previous work has used arbitrated dispute trees as a methodology for explanation. Those are dispute trees where nodes are seen as losing or winning depending on the outcome for the new case under consideration. In this work we show how arbitrated dispute trees can be readapted for different inputs, which allows a broader interpretation of them, capturing more of the input-output behaviour of the model. We show this readaptation is correct by construction, and thus the resulting reasoning based on this reuse is monotonic and thus necessarily a faithful explanation.
Date Issued
2022-09-11
Date Acceptance
2022-09-01
Citation
CEUR Workshop Proceedings, 2022, 3209, pp.1-12
ISSN
1613-0073
Publisher
CEUR Workshop Proceedings
Start Page
1
End Page
12
Journal / Book Title
CEUR Workshop Proceedings
Volume
3209
Copyright Statement
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
License URL
Identifier
https://ceur-ws.org/Vol-3209/
Source
1st International Workshop on Argumentation for eXplainable AI co-located with 9th International Conference on Computational Models of Argument (COMMA 2022)
Publication Status
Published
Start Date
2022-09-12
Coverage Spatial
Cardiff, Wales
Date Publish Online
2022-09-11