Maintaining prediction quality under the condition of a growing knowledge space
This addresses a foundational issue in AI for agents that rely on knowledge spaces to predict environments, though it appears incremental as it builds on existing concepts of knowledge management.
The paper tackles the problem of maintaining high-quality knowledge spaces as they grow, proposing a mathematical model to describe how quality evolves based on error rates, propagation, and countermeasures, and showing that quality collapses if low-quality knowledge is removed too slowly relative to growth.
Intelligence can be understood as an agent's ability to predict its environment's dynamic by a level of precision which allows it to effectively foresee opportunities and threats. Under the assumption that such intelligence relies on a knowledge space any effective reasoning would benefit from a maximum portion of useful and a minimum portion of misleading knowledge fragments. It begs the question of how the quality of such knowledge space can be kept high as the amount of knowledge keeps growing. This article proposes a mathematical model to describe general principles of how quality of a growing knowledge space evolves depending on error rate, error propagation and countermeasures. There is also shown to which extend the quality of a knowledge space collapses as removal of low quality knowledge fragments occurs too slowly for a given knowledge space's growth rate.