Biomimetic Approach to Tacit Learning Based on Compound Control
Biomimetic Approach to Tacit Learning Based on Compound Control
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DOI：
10.1109/tsmcb.2009.2014470
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发表时间：
2010-02-01
影响因子：
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通讯作者：
Kimura, Hidenori
中科院分区：
文献类型：
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作者：
Shimoda, Shingo;Kimura, Hidenori
生物体适应未知环境的卓越能力归因于与当前人工机器学习范式完全不同的学习机制。由相同元素组成、具有简单活动规则的计算介质在生物控制中发挥着重要作用，例如大脑中神经元的活动和细胞内控制中的分子相互作用。作为计算媒体个体活动整合的结果，新的行为模式出现以适应不断变化的环境。我们之前以机器学习的形式实现了生物控制的这一功能，并成功实现了无需机器人模型或轨迹规划的双足行走。尽管双足行走取得了成功，但计算媒体的个体活动为何能够实现全局行为仍然是一个谜。在本文中，我们通过采用统计方法来回答这个问题，该方法将计算媒体的个体活动与全球网络行为联系起来。我们表明，个体活动可以从特定的全局角度生成优化的行为，即自主节奏生成和平衡姿势的学习，而无需使用全局性能指数。
The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.