In this section
@inproceedings{Yin:2024,
author = {Yin, X and Potyka, N and Toni, F},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
title = {CE-QArg: Counterfactual explanations for quantitative bipolar argumentation frameworks},
year = {2024}
}
TY - CPAPER
AB - There is a growing interest in understanding arguments’strength in Quantitative Bipolar Argumentation Frameworks(QBAFs). Most existing studies focus on attribution-basedmethods 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.
AU - Yin,X
AU - Potyka,N
AU - Toni,F
PB - International Joint Conferences on Artificial Intelligence Organization
PY - 2024///
TI - CE-QArg: Counterfactual explanations for quantitative bipolar argumentation frameworks
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
To reach the elected speaker of the network, Dr Rossella Arcucci, please contact:
To reach the network manager, Diana O'Malley - including to join the network - please contact: