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Constrained Derivation in Assumption-Based Argumentation

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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

Structured argumentation formalisms provide a rich framework to formalise and reason over situations where contradicting information is present. However, in most formalisms the integral step of constructing all possible arguments is performed in an unconstrained way. For this, it may not be possible to represent situations where the reasoning process is subject to various kinds of restrictions; for example, where the possibility of communication is limited in a multi-agent setting. In this work, we introduce a general approach that allows constraining the derivation of arguments for assumption-based argumentation. We show that, under certain conditions, this reduces to eliminating rules from the given knowledge base while letting the derivation of arguments unconstrained. For this as well as for the general approach to derivation constraining, we provide an encoding into Answer Set Programming.

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Notes

  1. 1.

    For a formal definition of ABA, we refer to Sect. 2.

  2. 2.

    Available at https://www.dbai.tuwien.ac.at/research/argumentation/abasp/.

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Acknowledgments

The authors thank the reviewers for their helpful comments to improve the original version of this paper. This research has been supported by Vienna Science and Technology Fund (WWTF) through project ICT19-065 and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101034440).

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Authors and Affiliations

  1. Institute of Logic and Computation, TU Wien, Vienna, Austria

    Giovanni Buraglio, Wolfgang Dvořák & Stefan Woltran

  2. Department of Computing, Imperial College London, London, UK

    Anna Rapberger

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  1. Giovanni Buraglio
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  2. Wolfgang Dvořák
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  3. Anna Rapberger
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  4. Stefan Woltran
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Correspondence to Giovanni Buraglio .

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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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Buraglio, G., Dvořák, W., Rapberger, A., Woltran, S. (2024). Constrained Derivation in Assumption-Based Argumentation. 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_19

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  • DOI: https://doi.org/10.1007/978-3-031-56940-1_19

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