Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
Sanyam Jain, Stefano Nichele
细胞自动机和其他离散动力学系统长期以来一直被研究为紧急复杂性的模型。 最近,神经细胞自动机被提出作为模型来研究更通用的人工智能的出现,这要归功于它们支持自我组织,出现和开放等属性的倾向。 然而,了解大规模系统中的紧急复杂性是一个开放的挑战。 如何识别导致紧急复杂结构和行为的重要计算? 在这项工作中,我们系统地研究了基于将宏态粗磨成较小块的1维和二维细胞自动机的降维形式。 我们讨论选定的示例,并在附录中提供具有不同过滤水平的粗粒的整个探索(也可以在此链接中以数字方式提供:https://s4nyam.github.io/eca88/)。 我们认为,能够捕捉人工智能系统中的新兴复杂性可能为开放式进化铺平道路,这是实现人工智能的一条合理途径。
Cellular automata and other discrete dynamical systems have long been studied as models of emergent complexity. Recently, neural cellular automata have been proposed as models to investigate the emerge of a more general artificial intelligence, thanks to their propensity to support properties such as self-organization, emergence, and open-endedness. However, understanding emergent complexity in large scale systems is an open challenge. How can the important computations leading to emergent compl...