The Degrees of Monotony-Dilemma in Abstract Argumentation

Timotheus Kampik, Dov Gabbay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we introduce the notion of the degree of monotony to abstract argumentation, a well-established method for drawing inferences in face of conflicts in non-monotonic reasoning. Roughly speaking, the degree of monotony allows us, given an abstract argumentation semantics and an abstract argumentation framework to be as monotonic as possible, when iteratively drawing inferences and expanding the argumentation framework. However, we also show that when expanding an argumentation framework several times using so-called normal expansions, an agent may, at any given step, select a conclusion that has the highest degree of monotony w.r.t. the previous conclusion (considering the constraints of the semantics), but end up with a conclusion that has a suboptimal degree of monotony w.r.t. one or several conclusions that precede the previous conclusion. We formalize this observation as the degrees of monotony-dilemma.

Original languageEnglish
Title of host publicationSymbolic and Quantitative Approaches to Reasoning with Uncertainty - 16th European Conference, ECSQARU 2021, Proceedings
EditorsJirina Vejnarová, Nic Wilson
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783030867713
StatePublished - 2021
Event16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2021 - Prague, Czech Republic
Duration: 21 Sep 202124 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12897 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2021
Country/TerritoryCzech Republic

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Abstract argumentation
  • Argumentation dynamics
  • Non-monotonic reasoning


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