两篇新论文探讨了用于论证框架的高级分割技术，以提高计算效率。第一篇论文侧重于基于假设的论证框架（ABAFs），提出在知识库而非其图实例上进行分割，以处理计算复杂性。第二篇论文介绍了双极基于集合的论证框架（BSAFs）的新型分割方法，该框架结合了集体攻击和支持，并推广了现有技术。 AI
影响 这些论文引入了论证框架的新型计算技术，有可能提高依赖于逻辑推理和辩论建模的AI系统的效率。
排序理由 该集群包含两篇在arXiv上发表的学术论文。
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arXiv:2604.27964v1 Announce Type: new Abstract: Assumption-Based Argumentation (ABA) is a well-established formalism for modelling and reasoning over debates, with a wide range of applications. However, the high computational complexity of core reasoning tasks in ABA poses a sign…
arXiv:2604.28112v1 Announce Type: new Abstract: This work proposes novel splitting techniques for argumentation formalisms that incorporate supports between defeasible elements. We base our studies on bipolar set-based argumentation frameworks (BSAFs) which generalize argumentati…
This work proposes novel splitting techniques for argumentation formalisms that incorporate supports between defeasible elements. We base our studies on bipolar set-based argumentation frameworks (BSAFs) which generalize argumentation frameworks with collective attacks (SETAFs), …
Assumption-Based Argumentation (ABA) is a well-established formalism for modelling and reasoning over debates, with a wide range of applications. However, the high computational complexity of core reasoning tasks in ABA poses a significant challenge for its applicability. This is…