Repairing Assumption-Based Argumentation Frameworks

The field of formal argumentation is driven by situations where conflicting information need to be balanced out argumentatively. However, if the given knowledge base does not induce any reasonable viewpoint, these methods are stretched to their limits. In this paper, we address this issue in the context of assumption-based argumentation (ABA). More specifically, we study repairing notions for knowledge bases where no assumption can be accepted. We develop genuine repairing techniques for ABA, based on the modification of the building blocks of ABA frameworks, i.e., rules and assumptions. Thereby, we start from basic operators towards more and more fine-grained approaches. We compare their behavior to each other and demonstrate their compliance with suitable repairing desiderata.