We propose a novel approach to logic-based learning which generates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. These ABA frameworks can be mapped onto logic programs with negation as failure that may be non-stratified. Whereas existing argumentation-based methods learn exceptions to general rules by interpreting the exceptions as rebuttal attacks, our approach interprets them as undercutting attacks. Our learning technique is based on the use of transformation rules, including some adapted from logic program transformation rules (notably folding) as well as others, such as rote learning and assumption introduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we also propose a variant that handles the non-stratified case. We illustrate the benefits of our approach with a number of examples, which show that, on one hand, we are able to easily reconstruct other logic-based learning approaches and, on the other hand, we can work out in a very simple and natural way problems that seem to be hard for existing techniques.

翻译：我们提出了一种新颖的基于逻辑的学习方法，该方法利用给定的背景知识，从正例和反例中生成基于假设的论证（ABA）框架。这些ABA框架可映射为带有失败即否定的逻辑程序，这些程序可能不存在分层结构。现有的基于论证的方法通过将例外解释为反驳攻击来学习通用规则的例外情况，而我们的方法则将它们解释为削弱攻击。我们的学习技术基于转换规则的使用，包括一些改编自逻辑程序转换规则（特别是折叠规则）的规则，以及其他规则，如机械学习和假设引入。我们提出了一种通用策略，以适当的顺序应用转换规则来学习分层框架，并提出了一个处理非分层情况的变体。我们通过多个示例展示了我们方法的优势，这些示例表明，一方面，我们能够轻松地重建其他基于逻辑的学习方法，另一方面，我们能够以非常简单自然的方式解决现有技术似乎难以处理的问题。