Abstract
In this paper we question the ability of the existant ranking semantics for argumentation to capture persuasion settings, emphasizing in particular the phenomena of protocatalepsis (the fact that it is often efficient to anticipate the counter-arguments of the audience), and of fading (the fact that long lines of argumentation become ineffective). It turns out that some widely accepted principles of ranking-based semantics are incompatible with a faithful treatment of these phenomena. We thus propose a parametrized semantics based on propagation of values, which allows to control the scope of arguments to be considered for evaluation. We investigate its properties (identifying in particular threshold values guaranteeing that some properties hold), and report experimental results showing that the family of rankings that may be returned have a high coherence rate.
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Notes
- 1.
The generation algorithms are based on the three algorithms used for producing the benchmarks of the competition ICCMA’15 (see http://argumentationcompetition.org/2015/results.html).
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This work benefited from the support of the project AMANDE ANR-13-BS02-0004 of the French National Research Agency (ANR).
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Bonzon, E., Delobelle, J., Konieczny, S., Maudet, N. (2017). A Parametrized Ranking-Based Semantics for Persuasion. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_17
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