Assessing the Impact of Agents in Weighted Bipolar Argumentation Frameworks

Areski Himeur, Bruno Yun* (Corresponding Author), Pierre Bisquert, Madalina Croitoru

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

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Argumentation provides a formalism consisting of arguments and attacks/supports between these arguments and can be used to rank or deduce justified conclusions. In multi-agent settings, where several agents can advance arguments at the same time, understanding which agent has the most influence on a particular argument can improve an agent’s decision about which argument to advance next. In this paper, we introduce an argumentation framework with authorship and define new semantics to account for the impact of the agents on the arguments. We propose a set of desirable principles that such a semantics should satisfy, instantiate such semantics from two popular graded based semantics, and study to which extent these principles are satisfied. These semantics will allow an observer to identify the most influential agents in a debate.
Original languageEnglish
Title of host publicationArtificial Intelligence XXXVIII
Subtitle of host publication41st SGAI International Conference on Artificial Intelligence, AI 2021, Cambridge, UK, December 14–16, 2021, Proceedings
Number of pages14
ISBN (Electronic)978-3-030-91100-3
ISBN (Print)978-3-030-91099-0
Publication statusPublished - Dec 2022
EventAI-2021 : Forty-first SGAI International Conference on Artificial Intelligence - Virtual , Cambridge, United Kingdom
Duration: 14 Dec 202116 Dec 2021
Conference number: 41st

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Country/TerritoryUnited Kingdom
Internet address


  • Argumentation
  • Graded semantics
  • Authorship


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