Fuzzy Labeling for Abstract Argumentation: An Empirical Evaluation

Abstract : Argumentation frameworks have to be evaluated with respect to argumentation semantics to compute the set(s) of accepted arguments. In a previous approach, we proposed a fuzzy labeling algorithm for computing the (fuzzy) set of acceptable arguments, when the sources of the arguments in the argumentation framework are only partially trusted. The convergence of the algorithm was proved, and the convergence speed was estimated to be linear, as it is generally the case with iterative methods. In this paper, we provide an experimental validation of this algorithm with the aim of carrying out an empirical evaluation of its performance on a benchmark of argumentation graphs. Results show the satisfactory performance of our algorithm, even on complex graph structures as those present in our benchmark.
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Steven Schockaert; Pierre Senellart. Tenth International Conference on Scalable Uncertainty Management (SUM 2016) , Sep 2016, Nice, France. Springer, Lecture Notes in Artificial Intelligence, pp.126 - 139, 2016, Scalable Uncertainty Management - 10th International Conference, SUM 2016, Nice, France, September 21--23, 2016, Proceedings. <http://sum2016.unice.fr/>. <10.1007/978-3-319-45856-4_9>
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Célia Da Costa Pereira, Mauro Dragoni, Andrea G. B. Tettamanzi, Serena Villata. Fuzzy Labeling for Abstract Argumentation: An Empirical Evaluation. Steven Schockaert; Pierre Senellart. Tenth International Conference on Scalable Uncertainty Management (SUM 2016) , Sep 2016, Nice, France. Springer, Lecture Notes in Artificial Intelligence, pp.126 - 139, 2016, Scalable Uncertainty Management - 10th International Conference, SUM 2016, Nice, France, September 21--23, 2016, Proceedings. <http://sum2016.unice.fr/>. <10.1007/978-3-319-45856-4_9>. <hal-01377557>

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