Epistemic Skills: Reasoning about Knowledge and Oblivion
Xiaolong Liang, Yì N. Wáng
TL;DR
The paper develops weighted epistemic logics with implicit epistemic skills to model knowledge updates, knowability, and forgettability in both individual and group contexts, introducing operators for upskilling, downskilling, reskilling, and learning. It provides a precise syntax (including $\mathcal{L}_{CDEF+-=\equiv\boxplus\boxminus\Box}$) and a robust semantics on weighted Kripke models, and it connects the framework to rough-set concepts for data-driven abstraction. The authors establish detailed complexity results: model checking is in P for logics without quantifiers (and remains P with updates), while quantified variants push to PSPACE-complete, and satisfiability ranges from PSPACE-complete (without common knowledge) to EXPTIME-complete (with common knowledge) depending on the presence of updates and quantifiers, with a suite of reductions underpinning these bounds. They also introduce variants (fuzzy sets and lattices) and a refined treatment of de re/de dicto readings, demonstrating the framework’s flexibility and potential applicability to data-intensive and dynamic epistemic settings.
Abstract
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.
