Bipolar Argumentation

Bipolar argumentation frameworks model reasoning processes involving both supporting and attacking arguments, aiming to determine the overall strength or acceptability of individual arguments within a complex system. Current research emphasizes developing and analyzing various semantics for these frameworks, particularly focusing on quantitative models and algorithms that incorporate attribution explanations, counterfactual analysis, and efficient computational methods for handling large or cyclic structures. This research is significant for advancing explainable AI, improving decision-making in applications like truth discovery and fraud detection, and providing a more nuanced understanding of human reasoning processes.