Spatial Complexity Measure for Characterising Cellular Automata Generated 2D Patterns

Javid Javaheri, Mohammad Ali; Blackwell, Tim; Zimmer, Robert and al-Rifaie, Mohammad Majid. 2015. Spatial Complexity Measure for Characterising Cellular Automata Generated 2D Patterns. Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings, pp. 210-212. [Article]

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Abstract or Description

Cellular automata (CA) are known for their capacity to generate complex patterns through the local interaction of rules. Often the generated patterns, especially with multi-state two-dimensional CA, can exhibit interesting emergent behaviour. This paper addresses quantitative evaluation of spatial characteristics of CA generated patterns. It is suggested that the structural characteristics of two-dimensional (2D) CA patterns can be measured using mean information gain. This information-theoretic quantity, also known as conditional entropy, takes into account conditional and joint probabilities of cell states in a 2D plane. The effectiveness of the measure is shown in a series of experiments for multi-state 2D patterns generated by CA. The results of the experiments show that the measure is capable of distinguishing the structural characteristics including symmetry and randomness of 2D CA patterns.

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Cellular automata, Spatial complexity, 2D patterns

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October 2015Published

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21 Mar 2016 10:45

Last Modified:

12 Oct 2023 13:14

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Yes, this version has been peer-reviewed.


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