Egbert Matthew D, Barandiaran Xabier E
School of Computer Science, University of Auckland, Auckland, New Zealand.
Te Ao Mārama, University of Auckland, Auckland, New Zealand.
Front Neurorobot. 2022 Dec 21;16:847054. doi: 10.3389/fnbot.2022.847054. eCollection 2022.
We suggest that the influence of biology in 'biologically inspired robotics' can be embraced at a deeper level than is typical, if we adopt an enactive approach that moves the focus of interest from how problems are solved to how problems emerge in the first place. In addition to being inspired by mechanisms found in natural systems or by evolutionary design principles directed at solving problems posited by the environment, we can take inspiration from the precarious, self-maintaining organization of living systems to investigate forms of cognition that are also precarious and self-maintaining and that thus also, like life, have their own problems that must be be addressed if they are to persist. In this vein, we use a simulation to explore precarious, self-reinforcing sensorimotor habits as a building block for a robot's behavior. Our simulations of simple robots controlled by an Iterative Deformable Sensorimotor Medium demonstrate the spontaneous emergence of different habits, their re-enactment and the organization of an ecology of habits within each agent. The form of the emergent habits is constrained by the sensory modality of the robot such that habits formed under one modality (vision) are more similar to each other than they are to habits formed under another (audition). We discuss these results in the wider context of: (a) enactive approaches to life and mind, (b) sensorimotor contingency theory, (c) adaptationist vs. structuralist explanations in biology, and (d) the limits of functionalist problem-solving approaches to (artificial) intelligence.
我们认为,如果我们采用一种生成式方法,将关注焦点从问题如何被解决转移到问题最初是如何出现的,那么在“生物启发式机器人技术”中,生物学的影响可以在比通常更深的层面上得到体现。除了受到自然系统中发现的机制或旨在解决环境提出的问题的进化设计原则的启发外,我们还可以从生命系统不稳定的、自我维持的组织中获取灵感,来研究同样不稳定且自我维持的认知形式,因此,就像生命一样,如果它们要持续存在,也有自己必须解决的问题。本着这种精神,我们使用模拟来探索不稳定的、自我强化的感觉运动习惯,将其作为机器人行为的一个构建模块。我们对由迭代可变形感觉运动介质控制的简单机器人的模拟,展示了不同习惯的自发出现、它们的重新 enactment 以及每个智能体内部习惯生态的组织。出现的习惯形式受到机器人感觉模态的限制,以至于在一种模态(视觉)下形成的习惯彼此之间比与在另一种模态(听觉)下形成的习惯更相似。我们在以下更广泛的背景下讨论这些结果:(a) 关于生命和心智的生成式方法,(b) 感觉运动偶联理论,(c) 生物学中适应主义与结构主义的解释,以及 (d) 功能主义解决问题方法对(人工)智能的局限性。 (注:“enactment”在文中可能有特定含义,这里直接保留英文未翻译,因为不太明确准确的中文对应词,可根据实际专业内容进一步确定。)