Artificial intelligence system incorporating automatic model switching based on model parameter confidence sets

Abstract

Computer systems and associated methods are disclosed to implement a model executor that dynamically selects machine learning models for choosing sequential actions. In embodiments, the model executor executes and updates an active model to choose sequential actions. The model executor periodically initiates a recent model and updates the recent model along with the active model based on recently chosen actions and results of the active model. The model executor periodically compares respective confidence sets of the two models' parameters. If the two confidence sets are sufficiently divergent, a replacement model is selected to replace the active model. In embodiments, the replacement model may be selected from a library of past models based on their similarity with the recent model. In embodiments, past models that exceed a certain age or have not been recently used as the active model are removed from the library.