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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Notes
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Here, we suppose that the agent is optimistic. To represent a pessimistic behaviour, we should use the \(\min \) operator, for example.
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- 3.
The dataset is available at http://www.dmi.unipg.it/conarg/dwl/networks.tgz.
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- 5.
The Sophia Antipolis dataset is available at https://goo.gl/pN1M9r.
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da Costa Pereira, C., Dragoni, M., Tettamanzi, A.G.B., Villata, S. (2016). Fuzzy Labeling for Abstract Argumentation: An Empirical Evaluation. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_9
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