Integrating Task Descriptions in Lifelong Machine Learning for Enhanced Knowledge Retention and Transfer

A. SRINIVASA RAO

Authors

A. SRINIVASA RAO INDIA Author

Keywords:

Lifelong Machine Learning, Task Descriptions, Knowledge Retention, Catastrophic Forgetting, Artificial Intelligence

Abstract

Lifelong Machine Learning (LML) aims to develop systems that continuously learn over time, retaining knowledge and transferring it to new tasks. A key challenge in LML is ensuring that learned knowledge remains relevant and can be effectively applied to future tasks. This paper proposes integrating task descriptions as a means to enhance knowledge retention and transfer in LML systems. By leveraging detailed task descriptions, we can create a more structured knowledge base that facilitates the identification of relevant prior knowledge and its application to new tasks. Our approach demonstrates improved performance in various scenarios, showing that task descriptions significantly aid in mitigating catastrophic forgetting and enhancing knowledge transfer. We present empirical evidence through experiments on benchmark datasets, illustrating the efficacy of integrating task descriptions in LML.

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