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An Interleaving Semantics of the Timed Concurrent Language for Argumentation to Model Debates and Dialogue Games

Published online by Cambridge University Press:  05 July 2023

STEFANO BISTARELLI ,
CARLO TATICCHI Open the ORCID record for CARLO TATICCHI [Opens in a new window]  and
MARIA CHIARA MEO
STEFANO BISTARELLI
Affiliation:
University of Perugia, Perugia, Italy (e-mails: stefano.bistarelli@unipg.it, carlo.taticchi@unipg.it)
CARLO TATICCHI
Affiliation:
University of Perugia, Perugia, Italy (e-mails: stefano.bistarelli@unipg.it, carlo.taticchi@unipg.it)
MARIA CHIARA MEO
Affiliation:
University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy (e-mail: mariachiara.meo@unich.it)
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Abstract

Time is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents’ behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents to communicate and reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps, with maximum parallelism for time elapsing. Following this approach, only one of the enabled agents is executed at each moment. To demonstrate the capabilities of the language, we also show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents. Lastly, we present an implementation of the language that can be accessed via a web interface.

Keywords

computational argumentationconcurrencyinterleaving
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 6 , November 2023 , pp. 1307 - 1333
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

The authors are members of the INdAM Research group GNCS and Consorzio CINI. This work has been partially supported by: GNCS-INdAM, CUP E55F22000270001; Project RACRA, funded by Ricerca di Base 2018-2019, University of Perugia; Project FICO, funded by Ricerca di Base 2021, University of Perugia; Project BLOCKCHAIN4FOODCHAIN, funded by Ricerca di Base 2020, University of Perugia; Project GIUSTIZIA AGILE, CUP J89J22000900005.

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