Most existing computational tools for assumption-based argumentation (ABA) focus on so-called flat frameworks, disregarding the more general case. In this paper, we study an instantiation-based approach for reasoning in possibly non-flat ABA. We make use of a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs). By utilizing compilability theory, we establish that the constructed BAFs will in general be of exponential size. In order to keep the number of arguments and computational cost low, we present three ways of identifying redundant arguments. Moreover, we identify fragments of ABA which admit a poly-sized instantiation. We propose two algorithmic approaches for reasoning in possibly non-flat ABA. The first approach utilizes the BAF instantiation while the second works directly without constructing arguments. An empirical evaluation shows that the former outperforms the latter on many instances, reflecting the lower complexity of BAF reasoning. This result is in contrast to flat ABA, where direct approaches dominate instantiation-based approaches.

翻译：现有的大多数假设论证（ABA）计算工具都聚焦于所谓的平坦框架，而忽略了更一般的情况。本文研究了一种基于实例化的方法，用于在可能非平坦的ABA中进行推理。我们利用ABA与双极论证框架（BAF）之间保持语义的转换方法，通过可编译性理论证明了所构建的BAF通常具有指数级规模。为了控制论证数量和计算成本，我们提出了三种识别冗余论证的方法。此外，我们确定了允许多项式规模实例化的ABA片段。针对可能非平坦的ABA推理，我们提出了两种算法路径：第一种利用BAF实例化，第二种则直接运作而无需构建论证。实证评估表明，前者在多数实例上优于后者，这反映了BAF推理更低的计算复杂度。该结果与平坦ABA形成鲜明对比——在平坦ABA中，直接方法通常优于基于实例化的方法。