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Spatial Complexity Measure for Characterising Cellular Automata Generated 2D Patterns

Javid Javaheri, Mohammad Ali; Blackwell, Tim M.; 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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1007/978-3-319-23485-4_21

Keywords:

Cellular automata, Spatial complexity, 2D patterns

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
October 2015Published

Item ID:

17240

Date Deposited:

21 Mar 2016 10:45

Last Modified:

01 Aug 2018 04:31

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/17240

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