一句话：提出加权模型上的认知逻辑，以“认知技能”刻画知识获取与遗忘，并研究可认知性、可遗忘性及计算复杂度。
本文提出一类认知逻辑，旨在统一刻画知识获取与遗忘的动态过程，并融入群体知识概念。其方法基于加权模型，引入“认知技能”度量来表示与知识更新相关的认知能力：知识获取对应技能提升（upskilling），遗忘则对应技能下降（downskilling）。该框架进一步定义“可认知性”与“可遗忘性”，分别指通过技能提升获得知识的可能性与通过技能下降陷入遗忘的可能性，并支持对认知de re与de dicto表达式区别的精细分析。最后，论文考察了模型检测与可满足性问题的计算复杂度，揭示了该理论的计算基础与实践含义。
This paper presents a class of epistemic logics that captures the dynamics of acquiring knowledge and descending into oblivion, while incorporating concepts of group knowledge. The approach is grounded in a system of weighted models, introducing an ``epistemic skills'' metric to represent the epistemic capacities tied to knowledge updates. Within this framework, knowledge acquisition is modeled as a process of upskilling, whereas oblivion is represented as a consequence of downskilling. The framework further enables exploration of ``knowability'' and ``forgettability,'' defined as the potential to gain knowledge through upskilling and to lapse into oblivion through downskilling, respectively. Additionally, it supports a detailed analysis of the distinctions between epistemic de re and de dicto expressions. The computational complexity of the model checking and satisfiability problems is examined, offering insights into their theoretical foundations and practical implications.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@article{abs-2504-01733,
title = {Epistemic Skills: Reasoning about Knowledge and Oblivion},
author = {Xiaolong Liang and Yì N. Wáng},
journal = {Logical Methods in Computer Science},
volume = {Volume 22, Issue 2},
year = {2026},
doi = {10.46298/lmcs-22(2:21)2026}
}