We present a type of epistemic logics that encapsulates both the dynamics of acquiring knowledge (knowing) and losing information (forgetting), alongside the integration of group knowledge concepts. Our approach is underpinned by a system of weighted models, which introduces an "epistemic skills" metric to effectively represent the epistemic abilities associated with knowledge update. In this framework, the acquisition of knowledge is modeled as a result of upskilling, whereas forgetting is by downskilling. Additionally, our framework allows us to explore the concept of "knowability," which can be defined as the potential to acquire knowledge through upskilling, and facilitates a nuanced understanding of the distinctions between epistemic de re and de dicto expressions. We study the computational complexity of model checking problems for these logics, providing insights into both the theoretical underpinnings and practical implications of our approach.

翻译：我们提出一类认知逻辑，该逻辑不仅封装了获取知识（知晓）和丢失信息（遗忘）的动态过程，还整合了群体知识概念。我们的方法基于一个加权模型系统，该系统引入了一种“认知技能”度量，以有效表征与知识更新相关的认知能力。在此框架中，知识的获取被建模为技能提升的结果，而遗忘则通过技能降级实现。此外，我们的框架使我们能够探索“可知性”这一概念，其可定义为通过技能提升获取知识的潜力，并有助于细致理解认知的从物表达与从言表达之间的区别。我们研究了这些逻辑的模型检测问题的计算复杂性，为我们的方法的理论基础与实际应用提供了深入见解。