A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

Tsumori Kenji, Ozawa Seiichi

doi:10.1541/ieejeiss.130.21

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Other Title

動的環境下で複数タスクを学習するニューラルネットモデル
ドウテキカンキョウカデフクスウタスクオガクシュウスルニューラルネットモデル

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Description

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

Journal

IEEJ Transactions on Electronics, Information and Systems

IEEJ Transactions on Electronics, Information and Systems 130 (1), 21-28, 2010

The Institute of Electrical Engineers of Japan

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