Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.

翻译：论证是人工智能领域一个非常活跃的研究方向，关注于人类和/或智能体对话中所使用论据的表示与评估。形式论证系统的可接受性语义定义了论据接受或拒绝的准则。目前已开发出若干被称为论证求解器的软件系统，用于依据这些准则计算被接受/拒绝的论据，其中包括采用不可解释方法学习识别可接受论据的系统。本文提出了一种新颖的框架，采用归纳逻辑编程方法，以可解释的方式为多种抽象及结构化论证框架学习可接受性语义。通过实证评估，我们证明该框架性能优于现有论证求解器，从而为形式论证与人机对话领域开辟了新的未来研究方向。