Abstract
The expressiveness of any given formalism lays the theoretical foundation for more specialized topics such as investigating dynamic reasoning environments. The modeling capabilities of the formalism under investigation yield immediate (im)possibility results in such contexts. In this paper we investigate the expressiveness of assumption-based argumentation (ABA), one of the major structured argumentation formalisms. In particular, we examine so-called signatures, i.e., sets of extensions that can be realized under a given semantics. We characterize the signatures of common ABA semantics for flat, finite frameworks with and without preferences. We also give several results regarding conclusion-based semantics for ABA.
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Notes
- 1.
We refer to Sect. 2 for a formal introduction of the semantics we consider.
- 2.
We note that the original translation slightly deviates from this version.
- 3.
Implementation of the canonical constructions for all semantics considered in this paper are available at https://pyarg.npai.science.uu.nl/ [28].
- 4.
We refer the interested reader to [7] for an in-depth study on forgetting in flat ABA.
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Acknowledgements
This research has been supported by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research “Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig”, project identification number: ScaDS.AI. Anna Rapberger was partially funded by the Vienna Science and Technology Fund (WWTF) through project ICT19-065, by the Austrian Science Fund (FWF) through project P32830, and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101020934).
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Berthold, M., Rapberger, A., Ulbricht, M. (2023). On the Expressive Power of Assumption-Based Argumentation. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_11
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