Epistemic Skills: Reasoning about Knowledge and Oblivion
This work addresses foundational issues in formal epistemology and AI for researchers in logic and knowledge representation, though it appears incremental as it builds on existing epistemic logics with new metrics.
The paper tackles the problem of modeling knowledge acquisition and oblivion dynamics in epistemic logic by introducing a weighted model system with an 'epistemic skills' metric, resulting in a framework that analyzes knowability, forgettability, and distinctions in epistemic expressions, with computational complexity examined for model checking and satisfiability.
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.