论文标题
一种发育性神经机制方法，用于增强手写数字的识别
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
论文作者
论文摘要
发育心理学和神经成像研究确定了数字和手指之间的紧密联系，这可以提高儿童的初始数量知识。最近的证据表明，对儿童体现策略的模拟也可以改善机器智能。本文探讨了体现策略在发育神经机制的背景下在卷积神经网络模型中的应用，在这种情况下，在操作时可能会逐渐获得培训信息，而不是丰富且充分作为经典的机器学习场景。实验分析表明，机器人手指的本体感受信息可以提高识别手写阿拉伯数字的网络精度，而训练示例和时期很少。该结果可与幼儿大脑成像和纵向研究相媲美。总之，在人造代理的培训中，这些发现还支持实施方案的相关性，并为人性化的学习过程提供了一种可能的方式，在该过程中，机器人的身体可以表达人工智能的内部过程，从而使其对人类更容易理解。
Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents' training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans.
