research-article

Designing multi-rover emergent specialization

Authors:

Geoff Nitschke,

Martijn SchutAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 233 - 240

https://doi.org/10.1145/1389095.1389132

Published: 12 July 2008 Publication History

Abstract

We compare the efficacy of the Enforced Sub-Populations (ESP) and Collective Neuro-Evolution (CONE) methods for designing behavioral specialization in a multi-rover collective behavior task. These methods are tested for Artificial Neural Network (ANN) controller design in an extension of the multi-rover task, where behavioral specialization is known to benefit task performance. The task is for multiple simulated autonomous vehicles (rovers) to maximize the detection of points of interest (red rocks) in a virtual environment. The task requires rovers to collectively sense such points of interest in order for them to be detected. Results indicate that the CONE method facilitates a level of specialization appropriate for achieving a significantly higher task performance, comparative to rover teams evolved by the ESP method.

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Cited By

Trujillo LOlague GLutton EFernández de Vega FDozal LClemente E(2011)Speciation in Behavioral Space for Evolutionary RoboticsJournal of Intelligent & Robotic Systems10.1007/s10846-011-9542-z64:3-4(323-351)Online publication date: 20-Jan-2011
https://doi.org/10.1007/s10846-011-9542-z
Nitschke G(2009)Neuro-evolution approaches to collective behaviorProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689804(1554-1561)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689804
Nitschke G(2009)Neuro-Evolution approaches to collective behavior2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983127(1554-1561)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983127
Show More Cited By

Index Terms

Designing multi-rover emergent specialization
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

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Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Trujillo LOlague GLutton EFernández de Vega FDozal LClemente E(2011)Speciation in Behavioral Space for Evolutionary RoboticsJournal of Intelligent & Robotic Systems10.1007/s10846-011-9542-z64:3-4(323-351)Online publication date: 20-Jan-2011
https://doi.org/10.1007/s10846-011-9542-z
Nitschke G(2009)Neuro-evolution approaches to collective behaviorProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689804(1554-1561)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689804
Nitschke G(2009)Neuro-Evolution approaches to collective behavior2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983127(1554-1561)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983127
van Krevelen DNitschke GRyan C(2008)Neuro-evolution for a gathering and collective construction taskProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389131(225-232)Online publication date: 13-Jul-2008
https://dl.acm.org/doi/10.1145/1389095.1389131

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