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A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms

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Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

Abstract

Designing controllers for a swarm of robots such that collaborative behaviour emerges at the swarm level is known to be challenging. Evolutionary approaches have proved promising, with attention turning more recently to evolving repertoires of diverse behaviours that can be used to compose heterogeneous swarms or mitigate against faults. Here we extend existing work by combining a Quality-Diversity algorithm (MAP-Elites) with a Genetic-Programming (GP) algorithm to evolve repertoires of behaviour-trees that define the robot controllers. We compare this approach with two variants of GP, one of which uses an implicit diversity method. Our results show that the QD approach results in larger and more diverse repertoires than the other methods with no loss in quality with respect to the best solutions found. Given that behaviour-trees have the added advantage of being human-readable compared to neural controllers that are typically evolved, the results provide a solid platform for future work in composing heterogeneous swarms.

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Notes

  1. 1.

    For an up-to-date list of relevant papers, see https://quality-diversity.github.io.

  2. 2.

    \(GP_{n,f,d}\) is allocated 75000 evaluations as it simultaneously solves 3 objectives.

  3. 3.

    Given the obvious difference between the QD and EA methods, we only show the QD containers given page limit constraints.

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Author information

Authors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Kirsty Montague, Emma Hart & Ben Paechter

  2. University of Capetown, Cape Town, South Africa

    Geoff Nitschke

Authors
  1. Kirsty Montague
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  2. Emma Hart
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  3. Geoff Nitschke
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  4. Ben Paechter
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Corresponding author

Correspondence to Emma Hart .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Montague, K., Hart, E., Nitschke, G., Paechter, B. (2023). A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_10

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