Abstract
Model-based learning systems such as neural networks usually “forget” learned skills due to incremental learning of new instances. This is because the modification of a parameter interferes with old memories. Therefore, to avoid forgetting, incremental learning processes in these learning systems must include relearning of old instances. The relearning process, however, is time-consuming. We present two types of incremental learning method designed to achieve quick adaptation with low resources. One approach is to use a sleep phase to provide time for learning. The other one involves a “meta-learning module” that acquires learning skills through experience. The system carries out “reactive modification” of parameters not only to memorize new instances, but also to avoid forgetting old memories using a meta-learning module.
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This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004
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Yamauchi, K., Oohira, T. & Omori, T. Fast incremental learning methods inspired by biological learning behavior. Artif Life Robotics 9, 128–134 (2005). https://doi.org/10.1007/s10015-004-0325-5
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DOI: https://doi.org/10.1007/s10015-004-0325-5