@article{Speaker_MacCluer_2011, title={Using Random Sets to Model Learning in Manufacturing}, volume={3}, url={https://www.rgnpublications.com/journals/index.php/jims/article/view/51}, DOI={10.26713/jims.v3i3.51}, abstractNote={It is widely observed that  manufacturing quality metrics improve as experience is gained during production. The traditional empirical <em>learning curves</em> 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.}, number={3}, journal={Journal of Informatics and Mathematical Sciences}, author={Speaker, Paul and MacCluer, C. R.}, year={2011}, month={Dec.}, pages={201–210} }