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On Naive Labellings – Realizability, Construction and Patterns of Redundancy

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Foundations of Information and Knowledge Systems (FoIKS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14589))

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Abstract

This paper is engaged with realizability in the realm of abstract argumentation. In particular, we deal with naive labellings forming the basis for a well-established family of semantics, namely the naivity-based semantics. Consequently, characterizing this semantics is a crucial step towards delineating further naivety-based semantics such as stable and stage semantics. We provide easily verifiable criteria deciding whether a certain set of labellings can be the naive outcome of a Dungean framework. Apart from this we also present a standard construction in the affirmative case. This means, constructing a witnessing framework for a realizable set of naive labelling is possible paving the way for applications. Finally, we offer insights into representational freedom and prove a kernel-based characterization for strong equivalence, which substantially differ from its extension-based counterpart.

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Acknowledgments.

This work was supported by the German Research Foundation (DFG, BA 6170/3-1) and by the German Federal Ministry of Education and Research (BMBF, 01/S18026A-F) by funding the competence center for Big Data and AI “ScaDS.AI” Dresden/Leipzig.

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Authors and Affiliations

  1. Leipzig University, Leipzig, Germany

    Ringo Baumann & Anne-Marie Heine

  2. Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany

    Ringo Baumann & Anne-Marie Heine

Authors
  1. Ringo Baumann
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  2. Anne-Marie Heine
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Correspondence to Anne-Marie Heine .

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Editors and Affiliations

  1. Leibniz University Hannover, Hanover, Germany

    Arne Meier

  2. TU Wien, Wien, Austria

    Magdalena Ortiz

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Baumann, R., Heine, AM. (2024). On Naive Labellings – Realizability, Construction and Patterns of Redundancy. In: Meier, A., Ortiz, M. (eds) Foundations of Information and Knowledge Systems. FoIKS 2024. Lecture Notes in Computer Science, vol 14589. Springer, Cham. https://doi.org/10.1007/978-3-031-56940-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-56940-1_7

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  • Online ISBN: 978-3-031-56940-1

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