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Autonomous Specialization in a Multi-Robot System using Evolving Neural Networks

Autonomous Specialization in a Multi-Robot System using Evolving Neural Networks
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Author(s): Masanori Goka (Hyogo Prefectural Institute of Technology, Japan) and Kazuhiro Ohkura (Hiroshima University, Japan)
Copyright: 2011
Pages: 15
Source title: Gaming and Simulations: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-60960-195-9.ch403

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Abstract

Artificial evolution has been considered as a promising approach for coordinating the controller of an autonomous mobile robot. However, it is not yet established whether artificial evolution is also effective in generating collective behaviour in a multi-robot system (MRS). In this study, two types of evolving artificial neural networks are utilized in an MRS. The first is the evolving continuous time recurrent neural network, which is used in the most conventional method, and the second is the topology and weight evolving artificial neural networks, which is used in the noble method. Several computer simulations are conducted in order to examine how the artificial evolution can be used to coordinate the collective behaviour in an MRS.

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