Information gain measure for structural discrimination of cellular automata configurations

Cellular automata (CA) are known for their capability in exhibiting interesting emergent behaviour and capacity to generate complex and often aesthetically appealing patterns through the local interaction of rules. Mean information gain has been suggested as a measure of discriminating structurally...

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Bibliographic Details
Published in2015 7th Computer Science and Electronic Engineering Conference (CEEC) pp. 47 - 52
Main Authors Javid, Mohammad Ali Javaheri, Blackwell, Tim, Zimmer, Robert, al-Rifaie, Mohammad Majid
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2015
Subjects
Atmospheric measurements
Automata
Complexity theory
Entropy
Gain measurement
Particle measurements
Uncertainty
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DOI10.1109/CEEC.2015.7332698

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Summary:Cellular automata (CA) are known for their capability in exhibiting interesting emergent behaviour and capacity to generate complex and often aesthetically appealing patterns through the local interaction of rules. Mean information gain has been suggested as a measure of discriminating structurally different two-dimensional (2D) patterns. This paper addresses quantitative evaluation of the complexity of CA generated configurations. In particular, we examine information gain as a spatial complexity measure for discriminating multi-state 2D CA generated configurations. 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 symmetries and randomness of 2D CA patterns.
DOI:10.1109/CEEC.2015.7332698