This technical report introduces a novel method, CE-QArg, for generating counterfactual explanations in Quantitative Bipolar Argumentation Frameworks (QBAFs). Unlike existing attribution-based methods that explain argument strength by assigning importance scores to other arguments, CE-QArg focuses on identifying how to change the current strength of an argument to a desired one.
The report begins by defining three counterfactual problems for QBAFs: strong, δ−approximate, and weak, each with varying levels of strictness in achieving the desired strength. It then delves into the challenges of finding cost-effective counterfactuals, particularly in cyclic QBAFs where closed-form expressions for argument strength are elusive.
The authors propose an iterative algorithm, CE-QArg, designed to find valid and cost-effective counterfactuals for the δ−approximate problem. This algorithm leverages two core modules: polarity, which determines the direction of base score updates based on argument relationships, and priority, which assigns higher updating magnitudes to arguments closer to the target argument.
The report further discusses formal properties of counterfactual explanations, including existence, alteration existence, nullified-validity, and related-validity. These properties provide theoretical grounding for the proposed approach and offer insights into the behavior of counterfactuals in QBAFs.
Finally, the authors present empirical evaluations of CE-QArg, demonstrating its effectiveness, scalability, and robustness through ablation studies and experiments on both acyclic and cyclic QBAFs. The results highlight the algorithm's ability to identify valid counterfactuals with lower costs compared to baseline methods.
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by Xiang Yin, N... at arxiv.org 11-12-2024
https://arxiv.org/pdf/2407.08497.pdfDeeper Inquiries