Paper Info
Title
Evolving Structures in 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.
Year
DOI
Venue
2019
10.1109/SSCI44817.2019.9002840
2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Keywords
Field
DocType
complex system,complexity,evolution,cellular automata,emergence,artificial intelligence
Artificial life,Complex system,Cellular automaton,Structural complexity,Computer science,Computational model,Artificial intelligence,Data compression,Artificial neural network,Grid
Conference
ISSN
ISBN
Citations 
Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence
978-1-7281-2486-5
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Hugo Cisneros100.34
Josef Sivic29653513.44
Tomas Mikolov312984573.44