LATIDIA · Investigación

Aprendizaje continuo homeostático

arXiv:2609.13771v1 Tipo de Anuncio: nuevo Resumen: En este artículo, formulo un problema de Aprendizaje Continuo y propongo un método llamado "Aprendizaje Continuo Homeostático" que permite a un agente de IA aprender continuamente en un chan

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arXiv:2609.13771v1 Announce Type: new Abstract: In this paper, I formulate a Continual Learning problem and propose a method named "Homeostatic Continual Learning" that enables an AI agent to learn continuously in a changing environment without catastrophic forgetting. The core of the method is to find outliers in the environment data when the agent experiences an outlier in its output. Through this method, the agent gradually completes its model and policy and performs well in more and more contexts. I also suggest that we may use the method to build a world model where the agent factorizes the objects in the world into features, abstract objects into comparable instances of concepts