  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.
