Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming. It allows capturing defeasible knowledge, subject to argumentative debate. While, in much existing work, ABA frameworks are given up-front, in this paper we focus on the problem of automating their learning from background knowledge and positive/negative examples. Unlike prior work, we newly frame the problem in terms of brave reasoning under stable extensions for ABA. We present a novel algorithm based on transformation rules (such as Rote Learning, Folding, Assumption Introduction and Fact Subsumption) and an implementation thereof that makes use of Answer Set Programming. Finally, we compare our technique to state-of-the-art ILP systems that learn defeasible knowledge.

翻译：假设基础论证（ABA）被提倡作为包括逻辑编程在内的多种非单调推理形式的统一形式化框架。它能够捕捉可废止知识，并接受论证性辩论。尽管在现有研究中，ABA框架通常是预先给定的，本文则聚焦于从背景知识和正/负示例中自动化学习这些框架的问题。与先前工作不同，我们首次在ABA稳定扩展下的勇敢推理框架中重新定义该问题。我们提出了一种基于转换规则（如机械学习、折叠、假设引入和事实蕴含）的新算法及其实现，该实现利用了答案集编程技术。最后，我们将本技术与学习可废止知识的前沿归纳逻辑编程系统进行了比较。