Splitting Assumption-Based Argumentation Frameworks
Giovanni Buraglio, Wolfgang Dvorak, Stefan Woltran

TL;DR
This paper explores a novel splitting approach on the knowledge base of Assumption-Based Argumentation Frameworks to improve computational reasoning efficiency, extending existing methods to their parametrised versions.
Contribution
It introduces a splitting technique directly on the knowledge base of ABA frameworks and generalises it to a parametrised form, addressing computational challenges.
Findings
Splitting on the knowledge base can reduce reasoning complexity in ABA.
The approach generalises existing splitting methods to their parametrised versions.
Experimental results show improved efficiency in reasoning tasks.
Abstract
Assumption-Based Argumentation (ABA) is a well-established formalism for modelling and reasoning over debates, with a wide range of applications. However, the high computational complexity of core reasoning tasks in ABA poses a significant challenge for its applicability. This issue is further aggravated 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 knowledge representation and 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, by restricting the search space to sub-frameworks only, and then combining the…
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