In this article, we present a novel approximation approach for abstract argumentation using a customized Graph Convolutional Network (GCN) architecture and a tailored training method. Our approach demonstrates promising results in approximating abstract argumentation tasks across various semantics, setting a new state of the art for performance on certain tasks. We provide a detailed analysis of approximation and runtime performance and propose a new scheme for evaluation. By advancing the state of the art for approximating the acceptability status of abstract arguments, we make theoretical and empirical advances in understanding the limits and opportunities for approximation in this field. Our approach shows potential for creating both general purpose and task-specific approximators and offers insights into the performance differences across benchmarks and semantics.

中文翻译：

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用图卷积网络逼近抽象论证中的问题


在本文中，我们使用定制的图卷积网络（GCN）架构和定制的训练方法，提出了一种用于抽象论证的新颖的近似方法。我们的方法在跨各种语义的近似抽象论证任务方面展示了有希望的结果，为某些任务的性能设定了新的技术水平。我们提供了近似和运行时性能的详细分析，并提出了一种新的评估方案。通过推进近似抽象论证可接受状态的最先进技术，我们在理解该领域近似的局限性和机会方面取得了理论和经验上的进展。我们的方法显示了创建通用和特定任务逼近器的潜力，并提供了对基准和语义之间的性能差异的见解。