  Explainable Artificial Intelligence and Formal Argumentation have received
significant attention in recent years. Argumentation-based systems often lack
explainability while supporting decision-making processes. Counterfactual and
semifactual explanations are interpretability techniques that provide insights
into the outcome of a model by generating alternative hypothetical instances.
While there has been important work on counterfactual and semifactual
explanations for Machine Learning models, less attention has been devoted to
these kinds of problems in argumentation. In this paper, we explore
counterfactual and semifactual reasoning in abstract Argumentation Framework.
We investigate the computational complexity of counterfactual- and
semifactual-based reasoning problems, showing that they are generally harder
than classical argumentation problems such as credulous and skeptical
acceptance. Finally, we show that counterfactual and semifactual queries can be
encoded in weak-constrained Argumentation Framework, and provide a
computational strategy through ASP solvers.
