In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features—that is, values that summarize a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way.

中文翻译：

---

预测模型和抽象论证：高复杂度语义的案例

在本文中，我们描述了如何在抽象论证中积极利用预测模型。特别是，我们提出了两组主要的结果。一方面，我们表明预测模型对于执行算法选择是有效的，以确定哪种方法更好地枚举给定论证框架的首选扩展。另一方面，我们展示了预测模型预测了首选扩展枚举问题解决方案的重要方面。通过利用一组广泛的论证框架特征（即总结框架潜在重要属性的值），所提出的方法能够准确预测哪种算法在给定问题实例上更快，以及解决方案的结构，这种结构的完整知识需要解决一个计算上的难题。提高现有的基于论证的系统支持人类感知和决策过程的能力只是对以廉价方式获得的此类知识的可能利用之一。