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AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines

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Swarm Intelligence (ANTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

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Abstract

We present Arlequin, an off-line automatic design method that produces control software for robot swarms by combining behavioral neural-network modules generated via neuro-evolution. The neural-network modules are automatically generated once, in a mission-agnostic way, and are then automatically assembled into probabilistic finite-state machines to perform various missions. With Arlequin, our goal is to reduce the amount of human intervention that is required for the implementation or the operation of previously published modular design methods. Simultaneously, we assess whether neuro-evolution can be used in a modular design method to produce control software that crosses the reality gap satisfactorily. We present robot experiments in which we compare Arlequin with Chocolate, a state of the art modular design method, and EvoStick, a traditional neuro-evolutionary swarm robotics method. The preliminary results suggest that automatically combining neural-network modules into probabilistic finite-state machines is a promising approach to the automatic conception of control software for robot swarms.

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Acknowledgements

The experiments were conceived by the three authors and performed by AL and KH. The article was drafted by AL and revised by the three authors. The research was directed by MB.

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). MB acknowledges support from the Belgian Fonds de la Recherche Scientifique – FNRS.

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Authors and Affiliations

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

    Antoine Ligot, Ken Hasselmann & Mauro Birattari

Authors
  1. Antoine Ligot
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  2. Ken Hasselmann
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  3. Mauro Birattari
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Corresponding author

Correspondence to Mauro Birattari .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

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Ligot, A., Hasselmann, K., Birattari, M. (2020). AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_21

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