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.

本研究选择计算可信度作为应用领域，评估了异议论证推理的推理能力，并将其与模糊推理和专家系统进行了比较。结果表明，与基线相比，基于异议论证的模型表现更为稳健。这项研究扩展了先前工作，从而提高了异议论证作为处理数据和知识不确定性的一种复杂方法的普适性。

在真实世界的环境中，使用可辩证论证和数量数据进行比较和扩展

Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts

The work reported here introduces Defeasible Logic Programming (DeLP), a
formalism that combines results of Logic Programming and Defeasible
Argumentation. DeLP provides the possibility of representing information in the
form of weak rules in a declarative manner, and a defeasible argumentation
inference mechanism for warranting the entailed conclusions.
In DeLP an argumentation formalism will be used for deciding between
contradictory goals. Queries will be supported by arguments that could be
defeated by other arguments. A query q will succeed when there is an argument A
for q that is warranted, ie, the argument A that supports q is found undefeated
by a warrant procedure that implements a dialectical analysis.
The defeasible argumentation basis of DeLP allows to build applications that
deal with incomplete and contradictory information in dynamic domains. Thus,
the resulting approach is suitable for representing agent's knowledge and for
providing an argumentation based reasoning mechanism to agents.

这篇论文介绍了一种名为 Defeasible Logic Programming（DeLP）的形式化方法，它将 Logic Programming 和 Defeasible Argumentation 的结果结合起来，提供了一种在动态领域中处理不完整和矛盾信息的代理人知识表示和基于论证的推理机制。