Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks

ArXi:2603.06067v1 Announce Type: new Formal argumentation is being used increasingly in artificial intelligence as an effective and understandable way to model potentially conflicting pieces of information, called arguments, and identify so-called acceptable arguments depending on a chosen semantics. This paper deals with the specific context of Quantitative Bipolar Argumentation Frameworks (QBAF), where arguments have intrinsic weights and can attack or each other. In this context, we