Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks
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 support each other. In this context, we introduce a novel family of gradual semantics, called aggregative semantics. In order to deal with situations in which attackers and supporters do not play a symmetric role, and in contrast to modular semantics, we propose to aggregate...
| Authors | Yann Munro, Isabelle Bloch, Marie-Jeanne Lesot |
|---|---|
| Primary category | cs.AI |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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