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.
翻译:处理不确定、矛盾和模棱两可的信息仍然是人工智能(AI)的中心问题。因此,许多形式主义被提出或修改,以考虑非声调性,只有数量有限的著作和研究人员进行任何类比较。非声调形式主义允许根据新的证据,从房舍中收回先前的结论或主张,在处理不确定性时提供一些适当的灵活性。本研究文章侧重于评价错误的逻辑性逻辑性能力,一种特别为模拟非声调推理而设想的形式主义。此外,为了处理非声调性的推理和专家系统,只选择和采用有限的工程和研究人员进行任何种类的比较。非声调形式主义形式主义是一种允许根据新的证据,从房舍中收回先前的结论或主张,在处理不确定性时提供一些适当的灵活性。因此,在判断性判断性判断性判断性判断时,可以视为非声调性判断性判断性判断。推论模型旨在将信任性推理性推理推理推理性推理性推理,而不是模拟非声调推理推理推理性推理性推理法,用以将类似推理推理推理推理推理推理推理推理推理推理推理推理推理推理,比推理推理推理性推理推理推理推理,比推理推理推理推理推理,比推理,推理推理推理理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,推理,