Splitting Assumption-Based Argumentation Frameworks

Assumption-Based Argumentation (ABA) is a well-established rule-based formalism for modelling and reasoning in non-monotonic settings, with a wide range of applications. However, the high computational complexity of core reasoning tasks in ABA poses a significant challenge for its applicability in practice. This issue is further exacerbated when ABA frameworks (ABAFs) are instantiated into graph-based argumentation formalisms, such as Dung’s Argumentation Frameworks (AFs) and Argumentation Frameworks with Collective Attacks (SETAFs). In the context of non-monotonic reasoning, a key strategy to address computational intractability is to optimise reasoning over a given knowledge base through divide-and-conquer algorithms. A paradigmatic example of this approach is splitting, where extensions of a given framework are computed incrementally, i. e. restricting the search space to sub-frameworks only, and then combining the obtained results. This approach has been successfully applied to SETAFs in the literature. Furthermore, a parametrised version has been introduced for AFs under stable semantics. However, the exponential growth produced by the instantiation process might undermine the usefulness of splitting on the argument graphs induced by ABAFs. For this reason, there is a need for splitting-based algorithms tailored for ABA. To address this issue, our work investigates the concept of splitting for ABAFs under common semantics. Furthermore, we generalise splitting to its parametrised version both for SETAFs and ABAFs.

Authors

• Giovanni Buraglio

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