CE-QArg: Counterfactual explanations for quantitative bipolar argumentation frameworks
File(s)paper_id_133 (3).pdf (1.17 MB)
Accepted version
Author(s)
Yin, Xiang
Potyka, Nico
Toni, Francesca
Type
Conference Paper
Abstract
There is a growing interest in understanding arguments’
strength in Quantitative Bipolar Argumentation Frameworks
(QBAFs). Most existing studies focus on attribution-based
methods that explain an argument’s strength by assigning importance scores to other arguments but fail to explain how to change the current strength to a desired one. To solve this issue, we introduce counterfactual explanations for QBAFs. We discuss problem variants and propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg). CE-QArg can identify valid and cost-effective counterfactual explanations based on two core modules, polarity and priority, which help determine the updating direction and magnitude for each argument, respectively. We discuss some formal properties of our counterfactual explanations and empirically evaluate CE-QArg on randomly generated QBAFs.
strength in Quantitative Bipolar Argumentation Frameworks
(QBAFs). Most existing studies focus on attribution-based
methods that explain an argument’s strength by assigning importance scores to other arguments but fail to explain how to change the current strength to a desired one. To solve this issue, we introduce counterfactual explanations for QBAFs. We discuss problem variants and propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg). CE-QArg can identify valid and cost-effective counterfactual explanations based on two core modules, polarity and priority, which help determine the updating direction and magnitude for each argument, respectively. We discuss some formal properties of our counterfactual explanations and empirically evaluate CE-QArg on randomly generated QBAFs.
Date Acceptance
2024-07-11
Publisher
International Joint Conferences on Artificial Intelligence Organization
Copyright Statement
Subject to copyright. This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
Source
21st International Conference on Principles of Knowledge Representation and Reasoning
Publication Status
Accepted
Start Date
2024-11-02
Finish Date
2024-11-08
Coverage Spatial
Hanoi, Vietnam
Rights Embargo Date
10000-01-01