Towards predicting spatial complexity: a learning classifier system approach to the identification of cellular automata

Author

Bull, Larry ; Lawson, Ian ; Adamatzky, Andrew ; DeLacyCostello, Ben

Author_Institution

Fac. of Comput., Eng. & Math. Sci., West of England Univ., Bristol

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

136

Abstract

This paper presents a novel approach to the programming of automata-based simulation and computation using a machine learning technique. The identification of lattice-based automata for real-world applications is cast as a data mining problem. Our approach to achieving this is to use evolutionary computing and reinforcement learning with performance fed back indicating the predictive accuracy of future behaviour of the given system. The purpose of this work is to develop an approach to identifying automata rules that can achieve good performance using data from a variety of kinds of complex systems

Keywords

cellular automata; data mining; evolutionary computation; learning (artificial intelligence); pattern classification; automata rule identification; automata-based simulation programming; cellular automata identification; complex system; data mining problem; evolutionary computing; lattice-based automata identification; learning classifier system approach; machine learning technique; reinforcement learning; spatial complexity; Automatic programming; Computational modeling; Concurrent computing; Data mining; Differential equations; Lattices; Learning automata; Machine learning; Mathematical model; Mathematics;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554677

Filename

1554677