In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for measuring the complexity growth can be defined. This includes approaches based on compression algorithms and artificial neural networks. We believe such a metric can be useful for designing systems that could exhibit open-ended evolution, which itself might be a prerequisite for development of general artificial intelligence. We conduct experiments on 1D and 2D grid worlds and demonstrate that using the proposed metric we can automatically construct computational models with emerging properties similar to those found in the Conway's Game of Life, as well as many other emergent phenomena. Interestingly, some of the patterns we observe resemble forms of artificial life. Our metric of structural complexity growth can be applied to a wide range of complex systems, as it is not limited to cellular automata.
翻译:在本文中,我们提出了衡量细胞自爆等复杂系统中新兴模式复杂性增长的方法。我们讨论了如何界定衡量复杂增长的计量方法,其中包括基于压缩算法和人工神经网络的方法。我们认为,这种衡量方法可以有助于设计能够显示无限制演变的系统,而这种演变本身可能是发展一般人工智能的先决条件。我们在1D和2D网格世界进行实验,并表明,使用拟议的衡量标准,我们可以自动构建与康威生命游戏和其他许多新现象类似的新兴特性的计算模型。有趣的是,我们观察到的一些模式类似于人造生命的形式。我们的结构复杂增长的衡量标准可以应用于广泛的复杂系统,因为它并不限于细胞自动数据。