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Towards an Implementation of a Concurrent Language for Argumentation

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AIxIA 2020 – Advances in Artificial Intelligence (AIxIA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12414))

Abstract

While agent-based modelling languages naturally implement concurrency, the currently available languages for argumentation do not allow to explicitly model this type of interaction. In this paper we introduce a concurrent language for handling process arguing and communicating using a shared argumentation framework (reminding shared constraint store as in concurrent constraint). We introduce also basic expansions, contraction and revision procedures as main bricks for enforcement, debate, negotiation and persuasion.

This work was developed within project “RACRA18 - Knowledge Representation and Automated Reasoning 2018”, partially founded by Fondo Ricerca di Base 2018, University of Perugia, and project “Argumentation 360”, partially founded by Fondo Ricerca di Base 2017, University of Perugia.

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Notes

  1. 1.

    We introduce both U and \(Arg \subseteq U\) (not present in the original definition by Dung) for our convenience, since in the concurrent language that we will define in Sect. 4 we use an operator to dynamically add arguments from U to Arg.

  2. 2.

    Since the set of extensions \(S_\sigma (F)\) is finite, \(test_c(a,l,\sigma )\) and \(test_s(a,l,\sigma )\) are decidable.

  3. 3.

    ConArg website: http://www.dmi.unipg.it/conarg/.

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Authors and Affiliations

  1. University of Perugia, Perugia, Italy

    Stefano Bistarelli

  2. Gran Sasso Science Institute, L’Aquila, Italy

    Carlo Taticchi

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  1. Stefano Bistarelli
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  2. Carlo Taticchi
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Correspondence to Carlo Taticchi .

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Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Department of Informatics, Systems and C, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

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Bistarelli, S., Taticchi, C. (2021). Towards an Implementation of a Concurrent Language for Argumentation. In: Baldoni, M., Bandini, S. (eds) AIxIA 2020 – Advances in Artificial Intelligence. AIxIA 2020. Lecture Notes in Computer Science(), vol 12414. Springer, Cham. https://doi.org/10.1007/978-3-030-77091-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-77091-4_10

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