arXiv:2504.01733v5 Announce Type: replace 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 ``knowabili
The continuous evolution of AI capabilities necessitates more sophisticated theoretical frameworks to understand and manage how intelligent systems acquire and lose information. This paper addresses a core challenge in making AI systems more reliable and interpretable.
A strategic reader should care because improving the theoretical understanding of knowledge dynamics in AI is critical for designing more robust, autonomous, and ethically sound AI agents across various high-stakes applications. It underpins the trustworthiness and explainability of advanced AI.
This research introduces a novel logical framework for modeling AI knowledge dynamics, moving beyond simple data acquisition to include concepts of 'epistemic skills' and 'oblivion,' which provides a more nuanced foundation for future AI development. It shifts the analytical perspective on how AI processes information.
- · AI Researchers
- · Developers of Explainable AI
- · Autonomous System Architects
- · AI Ethics Committees
- · Opaque AI Systems
- · Static Knowledge Representation Models
More sophisticated and transparent AI knowledge management systems could be developed based on these theoretical insights.
This could lead to the creation of AI agents that can adapt their 'understanding' more dynamically, including recognizing and forgetting irrelevant or outdated information.
Enhanced epistemic logic may contribute to AI systems capable of self-correction and improved error identification based on their current 'knowledge state,' pushing towards more truly autonomous AI.
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Read at arXiv cs.AI