Under-Approximating Semantics in Clustered Assumption-Based Argumentation

Approaches to computational argumentation provide foundational ways to reason argumentatively within Artificial Intelligence (AI). The underlying formal approaches can oftentimes be classified into structured argumentation and abstract argumentation. The former prescribe rigorous workflows, starting from knowledge bases to finding arguments in favour and against claims under scrutiny, and drawing conclusions. Abstract argumentation provides formal semantics operating on arguments whose internal structure is hidden and only relations are kept for reasoning, resulting in so-called argumentation frameworks (AFs). In this work, we apply a form of existential abstraction on the prominent structured approach of assumption-based argumentation (ABA), leading to an interactive way of simplifying argumentation scenarios by abstracting irrelevant details, towards supporting explainability. Existential abstraction was shown to be promising in many areas of AI, including a recent work on AFs. We lift this approach to the structured level-which is, as we show, both not direct from AFs and can benefit from utilization of the internal structure of arguments. Among our contributions, we introduce existential abstraction on ABA via clustering assumptions, develop semantics on clustered ABA frameworks for reasoning on such clusterings, show differences to the level of AFs, and provide a prototype interactive tool that obtains faithful clusterings that do not lead to any spurious reasoning.