Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models
This work addresses forgetting issues for adaptive systems in horticulture, representing an incremental advance.
The paper tackles catastrophic forgetting in continual learning by using prediction-error driven memory consolidation, achieving successful transfer of greenhouse models from research to production facilities.
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory consolidation. In line with evidences from the cognitive science and neuroscience, memories are retained depending on their congruency with the prior knowledge stored in the system. This is estimated in terms of prediction error resulting from a generative model. Moreover, this AI system is transferred onto an innovative application in the horticulture industry: the learning and transfer of greenhouse models. This work presents a model trained on data recorded from research facilities and transferred to a production greenhouse.