Albantakis Larissa
Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA.
Entropy (Basel). 2021 Oct 28;23(11):1415. doi: 10.3390/e23111415.
Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various disciplines, including graph-theory, information-theory, and complex system science. Here, I review and compare a range of measures related to autonomy and intelligent behavior. To that end, I analyzed the structural, information-theoretical, causal, and dynamical properties of simple artificial agents evolved to solve a spatial navigation task, with or without a need for associative memory. By contrast to standard artificial neural networks with fixed architectures and node functions, here, independent evolution simulations produced successful agents with diverse neural architectures and functions. This makes it possible to distinguish quantities that characterize task demands and input-output behavior, from those that capture intrinsic differences between substrates, which may help to determine more stringent requisites for autonomous behavior and the means to measure it.
当涉及到自主性时，系统的内部结构重要吗？虽然对于自主性的严格、可量化定义仍未达成共识，但跨学科领域（包括图论、信息论和复杂系统科学）已经提出了多种候选度量和相关量。在这里，我回顾并比较了一系列与自主性和智能行为相关的度量。为此，我分析了为解决空间导航任务而进化的简单人工主体的结构、信息理论、因果和动态特性，这些人工主体有无关联记忆需求。与具有固定架构和节点功能的标准人工神经网络不同，这里的独立进化模拟产生了具有不同神经架构和功能的成功主体。这使得区分表征任务需求和输入输出行为的量与捕捉基质之间内在差异的量成为可能，这可能有助于确定自主行为更严格的必要条件及其测量方法。
