Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks

Researchers introduce a new family of gradual semantics called aggregative semantics for Quantitative Bipolar Argumentation Frameworks (QBAF) in AI systems. The approach uses a three-stage computation that separately aggregates attackers and supporters before combining them with argument weights, providing more interpretable and parametrisable AI reasoning systems.