论文标题
比较并扩展使用不辩论论证与现实世界中的定量数据的使用
Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts
论文作者
论文摘要
处理不确定，矛盾和模棱两可的信息仍然是人工智能（AI）的核心问题。结果，已经提出或改编了许多形式主义以考虑非单调性，只有有限的作品和研究人员在其中进行了任何形式的比较。非单调形式主义是一种可以根据新的证据从前提中撤回以前的结论或主张，在处理不确定性时提供了一些理想的灵活性。这篇研究文章着重于评估不辩论论证的推论能力，这是一种用于建模非单调推理的形式主义。除此之外，由于AI社区中的广泛和公认的用途，选择并用作处理非主持推理的非单调性推理的模糊推理和专家系统。计算信任被选为此类模型应用的领域。信任是一种不确定的构造，因此，将推理应用于信任的推理可以看作是非单调的。推理模型旨在将信任标量分配给Wikipedia项目的编辑。特别是，基于参数的模型表现出的鲁棒性比在基线上建立的知识库或数据集比在基线上建立的模型更具鲁棒性。这项研究通过剥削不诚实的论证及其与类似方法的比较来促进知识的体系。例如，这种方法的实际用途以及促进类似实验的模块化设计的实际用途被举例说明了，并且在GitHub上公开提供了各自的实现[120，121]。这项工作增加了以前的作品，从经验上增强了不诚信论证作为一种引人入胜的方法，以定量数据和不确定的知识来推理。
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.
