(untitled) (Journal of Informatics and Mathematical Sciences, 2011)
It is widely observed that manufacturing quality metrics improve as experience is gained during production. The traditional empirical learning curves modeling such improvements have recently been explained by a predictive model deduced from first principles, namely certain principles imported into artificial intelligence from statistical mechanics. However, this new learning model is limited to a finite lesson pool of paradigm shifts. This paper presents an extension to incremental learning using sampling based on the notion of dynamic random sets.