  Dealing with uncertain, contradicting, and ambiguous information is still a
central issue in Artificial Intelligence (AI). As a result, many formalisms
have been proposed or adapted so as to consider non-monotonicity, with only a
limited number of works and researchers performing any sort of comparison among
them. A non-monotonic formalism is one that allows the retraction of previous
conclusions or claims, from premises, in light of new evidence, offering some
desirable flexibility when dealing with uncertainty. This research article
focuses on evaluating the inferential capacity of defeasible argumentation, a
formalism particularly envisioned for modelling non-monotonic reasoning. In
addition to this, fuzzy reasoning and expert systems, extended for handling
non-monotonicity of reasoning, are selected and employed as baselines, due to
their vast and accepted use within the AI community. Computational trust was
selected as the domain of application of such models. Trust is an ill-defined
construct, hence, reasoning applied to the inference of trust can be seen as
non-monotonic. Inference models were designed to assign trust scalars to
editors of the Wikipedia project. In particular, argument-based models
demonstrated more robustness than those built upon the baselines despite the
knowledge bases or datasets employed. This study contributes to the body of
knowledge through the exploitation of defeasible argumentation and its
comparison to similar approaches. The practical use of such approaches coupled
with a modular design that facilitates similar experiments was exemplified and
their respective implementations made publicly available on GitHub [120, 121].
This work adds to previous works, empirically enhancing the generalisability of
defeasible argumentation as a compelling approach to reason with quantitative
data and uncertain knowledge.
