Labellings for assumption-based and abstract argumentation

The semantics of Assumption-Based Argumentation (ABA) frameworks are traditionally characterised as assumption extensions, i.e. sets of accepted assumptions. Assumption labellings are an alternative way to express the semantics of flat ABA frameworks, where one of the labels in, out, or undec is assigned to each assumption. They are beneficial for applications where it is important to distinguish not only between accepted and non-accepted assumptions, but further divide the non-accepted assumptions into those which are clearly rejected and those which are neither accepted nor rejected and thus undecided. We prove one-to-one correspondences between assumption labellings and extensions for the admissible, grounded, complete, preferred, ideal, semi-stable and stable semantics. We also show how the definition of assumption labellings for flat ABA frameworks can be extended to assumption labellings for any (flat and non-flat) ABA framework, enabling reasoning with a wider range of scenarios. Since flat ABA frameworks are structured instances of Abstract Argumentation (AA) frameworks, we furthermore investigate the relation between assumption labellings for flat ABA frameworks and argument labellings for AA frameworks. Building upon prior work on complete assumption and argument labellings, we prove one-to-one correspondences between grounded, preferred, ideal, and stable assumption and argument labellings, and a one-to-many correspondence between admissible assumption and argument labellings. Inspired by the notion of admissible assumption labellings we introduce committed admissible argument labellings for AA frameworks, which correspond more closely to admissible assumption labellings of ABA frameworks than admissible argument labellings do

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Peer reviewe

Developments in abstract and assumption-based argumentation and their application in logic programming

Logic Programming (LP) and Argumentation are two paradigms for knowledge representation and reasoning under incomplete information. Even though the two paradigms share common features, they constitute mostly separate areas of research. In this thesis, we present novel developments in Argumentation, in particular in Assumption-Based Argumentation (ABA) and Abstract Argumentation (AA), and show how they can 1) extend the understanding of the relationship between the two paradigms and 2) provide solutions to problematic reasoning outcomes in LP. More precisely, we introduce assumption labellings as a novel way to express the semantics of ABA and prove a more straightforward relationship with LP semantics than found in previous work. Building upon these correspondence results, we apply methods for argument construction and conflict detection from ABA, and for conflict resolution from AA, to construct justifications of unexpected or unexplained LP solutions under the answer set semantics. We furthermore characterise reasons for the non-existence of stable semantics in AA and apply these findings to characterise different scenarios in which the computation of meaningful solutions in LP under the answer set semantics fails.Open Acces