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Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy editors. This research contributes to the field of argumentation by employing a replicable modular design which is suitable for modelling reasoning under uncertainty applied to distinct real-world domains.
Rizzo L., Dondio P, Longo L. Exploring the potential of defeasible argumentation for quantitative inferences in real-world contexts: An assessment of computational trust Proceedings of The 28th Irish Conference on Artificial Intelligence and Cognitive Science, Volume: Vol-277, pp. 37-48, DOI: 10.21427/jb0g-bs68