Evolving Structures in Complex Systems

--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. Recent advances in machine learning and deep learning have had successes at reproducing some very complex feats traditionally thought to be only achievable by living beings. However, making these systems adaptable and capable of developing and evolving on their own remains a challenge that might be crucial for eventually developing AI with general learning capabilities (for example as is further discussed in [1]). Building systems that mimic some key aspects of the behavior of existing intelligent organisms (such as the ability to evolve, improve, adapt, etc.) might represent a promising path. Intelligent organisms -- e.g., human beings but also most living organisms if we consider a broad definition of intelligence -- are a form of spontaneously occurring, ever evolving complex systems that exhibit these kinds of properties [2].