AINeutralarXiv – CS AI · Mar 94/10
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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.