Mainzer Klaus
Carl von Linde Academy, Technical University Munich, Germany.
J Physiol Paris. 2009 Sep-Dec;103(3-5):296-304. doi: 10.1016/j.jphysparis.2009.08.012. Epub 2009 Aug 7.
After an introduction (1) the article analyzes the evolution of the embodied mind (2), the innovation of embodied robotics (3), and finally discusses conclusions of embodied robotics for human responsibility (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of future technology. We start with the distinction of symbolic and embodied AI (3.1) and explain embodied robots as dynamical systems (3.2). Self-organization needs self-control of technical systems (3.3). Cellular neural networks (CNN) are an example of self-organizing technical systems offering new avenues for neurobionics (3.4). In general, technical neural networks support different kinds of learning robots (3.5). Finally, embodied robotics aim at the development of cognitive and conscious robots (3.6).
在引言部分(1)之后,本文分析了具身认知的演变(2)、具身机器人技术的创新(3),最后讨论了具身机器人技术对人类责任的结论(4)。考虑到具身认知的演变(2),我们首先介绍复杂系统和非线性动力学(2.1),将这种方法应用于神经自组织(2.2),区分大脑的复杂程度(2.3),通过复杂系统动力学解释认知状态的出现(2.4),并讨论将大脑建模为复杂非线性系统的标准(2.5)。具身机器人技术的创新(3)是未来技术面临的一项挑战。我们首先区分符号人工智能和具身人工智能(3.1),并将具身机器人解释为动力系统(3.2)。自组织需要技术系统的自我控制(3.3)。细胞神经网络(CNN)是自组织技术系统的一个例子,为神经仿生学提供了新途径(3.4)。一般来说,技术神经网络支持不同类型的学习机器人(3.5)。最后,具身机器人技术旨在开发具有认知能力和意识的机器人(3.6)。