Evolving Structures in Complex Systems

TL;DR

This paper introduces a novel metric for measuring the growth of complexity in emergent patterns within complex systems, utilizing compression and neural network approaches, demonstrated through experiments on cellular automata and grid worlds.

Contribution

It proposes a new, versatile metric for complexity growth applicable to various complex systems, aiding in the design of systems with open-ended evolution.

Findings

The metric successfully identifies emergent properties in cellular automata.

Patterns resembling artificial life forms were observed using the metric.

The approach is applicable beyond cellular automata to other complex systems.

Abstract

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.