用技能模型模拟知识获取与遗忘过程，支持群体认知分析。
Epistemic Skills: Reasoning about Knowledge and Oblivion
- 引入加权模型与'认知技能'度量，量化知识更新能力
- 将知识获取视为技能提升，遗忘视为技能下降的自然结果
- 可分析认知表达差异，适合逻辑与认知科学研究者
本文提出一类刻画知识获取与遗忘动态的模态逻辑系统，融合群体知识概念。基于加权模型，引入‘认知技能’度量以表征知识更新能力。知识获取被建模为技能提升过程，遗忘则为技能退化后果。该框架支持对‘可知性’（可通过技能提升获得知识）与‘易忘性’（可通过技能下降陷入无知）的分析，并能细致区分认知命题中的de re与de dicto表达。同时考察了模型检验与可满足性问题的计算复杂性，揭示其理论基础与实际应用价值。
原文摘要 · Abstract (English)
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.
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