Autonomous cognition development with lifelong learning: A self-organizing and reflecting cognitive network

摘要

Lifelong learning is still a great challenge for cognitive robots since the continuous streaming data they encounter is usually enormous and no-stationary. Traditional cognitive methods suffer from large storage and computation consumption in this situation. Therefore, we propose a self-organizing and reflecting cognitive network (SORCN) to realize robotic lifelong cognitive development through incremental learning and regular reflecting. The network integrates a self-organizing incremental neural network (SOINN) with a modified CFS clustering algorithm. SOINN develops concise object concepts to alleviate storage consumption. Moreover, we modify SOINN by an efficient competitive method based on reflection results to reduce the learning computation. The modified CFS clustering algorithm is designed for reflecting knowledge learned by SOINN periodically. It improves the traditional CFS as a three-step clustering method including clustering, merging and splitting. Specifically, an autonomous center selection strategy is employed for CFS to cater to online learning. Moreover, a series of cluster merging and splitting strategies are proposed to enable CFS to cluster data incrementally and improve its clustering effect. Additionally, the reflection results are utilized to adjust the topological structure of SOINN and guide the future learning. Experimental results demonstrate that SORCN can achieve better learning effectiveness and efficiency over several state-of-art algorithms. (c) 2020 Elsevier B.V. All rights reserved.