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Evaluation of Alternative Exploration Schemes in the Automatic Modular Design of Robot Swarms

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Artificial Intelligence and Machine Learning (BNAIC 2019, BENELEARN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1196))

Abstract

The swarm robotics literature has shown that complex tasks can be solved by large groups of simple robots interacting with each other and their environment. Most of these tasks require the robots to explore their environment, making exploration a building block of the behaviors of robot swarms. However, exploration schemes have rarely been thoroughly evaluated, especially in the context of automatic design. This is the case with AutoMoDe, an automatic modular design approach that designs control software by assembling predefined mission-agnostic modules that embed fixed and arbitrarily selected exploration schemes. In this paper, we study the influence of different exploration schemes on the automatic design of robot swarms. To do so, we introduce AutoMoDe-Coconut, a new variant of AutoMoDe with multiple configurable exploration schemes embedded within its modules. We test Coconut both in bounded and unbounded workspaces and we compare the results with those of AutoMoDe-Chocolate in order to understand the impact of the new exploration schemes. The results show that Coconut is prone to select exploration schemes that fulfill the requirements of the mission in hand. However, Coconut does not perform better than Chocolate, even in situations where the only exploration schemes available to Chocolate are at an apparent disadvantage. We conjecture that the overall exploration capabilities of the swarm are not the mere reflection of individual-level exploration schemes but result from a more complex interaction between the atomic behaviors of the individuals.

GS and MK contributed equally to this work and should be considered as co-first authors. The software used in the experiments was implemented by GS and MK. The experiments were designed by DGR and performed by GS and MK. The paper was drafted by GS and MK and edited by MK and MB; all authors read and commented the final version. The research was directed by MB.

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Notes

  1. 1.

    Originally, this module was called exploration [14]. In this paper, we changed its denomination to avoid confusion with the notion of exploration scheme.

  2. 2.

    This protocol has been used in [13, 14, 18, 21, 22] and is further discussed in [3].

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Acknowledgements

The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 681872). Mauro Birattari acknowledges support from the Belgian Fonds de la Recherche Scientifique – FNRS. David Garzón Ramos acknowledges support from the Colombian Administrative Department of Science, Technology and Innovation – COLCIENCIAS.

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Gaëtan Spaey, Miquel Kegeleirs, David Garzón Ramos & Mauro Birattari

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  1. Gaëtan Spaey
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  2. Miquel Kegeleirs
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  3. David Garzón Ramos
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  4. Mauro Birattari
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Correspondence to Mauro Birattari .

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Editors and Affiliations

  1. Vrije Universiteit Brussel, Brussels, Belgium

    Bart Bogaerts

  2. Université Libre de Bruxelles, Brussels, Belgium

    Gianluca Bontempi

  3. Université de Liège, Liège, Belgium

    Pierre Geurts

  4. Vrije Universiteit Brussel, Brussels, Belgium

    Nick Harley

  5. Université Libre de Bruxelles, Brussels, Belgium

    Bertrand Lebichot

  6. Université Libre de Bruxelles, Brussels, Belgium

    Tom Lenaerts

  7. Université de Liège, Liège, Belgium

    Gilles Louppe

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Spaey, G., Kegeleirs, M., Garzón Ramos, D., Birattari, M. (2020). Evaluation of Alternative Exploration Schemes in the Automatic Modular Design of Robot Swarms. In: Bogaerts, B., et al. Artificial Intelligence and Machine Learning. BNAIC BENELEARN 2019 2019. Communications in Computer and Information Science, vol 1196. Springer, Cham. https://doi.org/10.1007/978-3-030-65154-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-65154-1_2

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