A Theory of Cheap Control in Embodied Systems.
A Theory of Cheap Control in Embodied Systems.
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DOI：
10.1371/journal.pcbi.1004427
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发表时间：
2015-09
影响因子：
4.3
通讯作者：
Ay N
中科院分区：
文献类型：
--
作者：
Montúfar G;Ghazi-Zahedi K;Ay N
我们提出了一个用于设计体现代理的廉价控制架构的框架。我们的推导以通用近似的经典问题为指导，由此我们探索了利用代理的体现来对感觉运动控制生成的行为进行新的、更有效的通用近似的可能性。将这种具体化通用近似与经典的非具体化通用近似进行比较。为了举例说明我们的方法，我们提出了根据条件限制玻尔兹曼机定义的政策模型的详细定量案例研究。与需要指数数量的参数的非体现通用近似相比，在体现设置中，我们能够使用更小的模型生成所有可能的行为，从而获得廉价的通用近似。我们用六足行走机通过实验测试并证实了该理论。实验表明，我们的理论预测的控制器复杂度接近最小充分值，这意味着该理论具有直接的实际意义。给定一个身体和一个环境，为了产生一组所需的行为，大脑的复杂性需要多少？一般的理解是，身体和环境的物理特性与所需的大脑复杂性相关。更准确地说，有人指出，自然进化的智能系统倾向于利用其体现约束，这使得它们能够用相对简洁的大脑表达复杂的行为。尽管这种简约控制的原则在很久以前就已被制定出来，但直到最近人们才开始发展一种形式主义，这种形式主义是在给定实施例约束的情况下对足够的大脑复杂性进行定量陈述所需的。在这项工作中，我们提出了一种精确的数学方法，将代理的物理和行为约束与所需的控制器复杂性联系起来。作为控制器架构，我们选择著名的人工神经网络，即条件限制玻尔兹曼机，并将其复杂度定义为隐藏单元的数量。我们用虚拟的六足行走生物进行了实验，这为理论预测的准确性提供了证据。
We present a framework for designing cheap control architectures of embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent’s embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the classical non-embodied universal approximation. To exemplify our approach, we present a detailed quantitative case study for policy models defined in terms of conditional restricted Boltzmann machines. In contrast to non-embodied universal approximation, which requires an exponential number of parameters, in the embodied setting we are able to generate all possible behaviors with a drastically smaller model, thus obtaining cheap universal approximation. We test and corroborate the theory experimentally with a six-legged walking machine. The experiments indicate that the controller complexity predicted by our theory is close to the minimal sufficient value, which means that the theory has direct practical implications. Given a body and an environment, what is the brain complexity needed in order to generate a desired set of behaviors? The general understanding is that the physical properties of the body and the environment correlate with the required brain complexity. More precisely, it has been pointed that naturally evolved intelligent systems tend to exploit their embodiment constraints and that this allows them to express complex behaviors with relatively concise brains. Although this principle of parsimonious control has been formulated quite some time ago, only recently one has begun to develop the formalism that is required for making quantitative statements on the sufficient brain complexity given embodiment constraints. In this work we propose a precise mathematical approach that links the physical and behavioral constraints of an agent to the required controller complexity. As controller architecture we choose a well-known artificial neural network, the conditional restricted Boltzmann machine, and define its complexity as the number of hidden units. We conduct experiments with a virtual six-legged walking creature, which provide evidence for the accuracy of the theoretical predictions.