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Task-Agnostic Evolution of Diverse Repertoires of Swarm Behaviours

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Swarm Intelligence (ANTS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

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Abstract

Quality diversity algorithms are evolutionary algorithms that aim to evolve diverse repertoires of high-quality solutions. Quality diversity has recently been used with considerable success to evolve repertoires of single-robot controllers in a wide range of applications. In this paper, we propose a methodology for the evolution of repertoires of general swarm behaviours. We use a quality diversity algorithm that relies on a behaviour characterisation and a quality metric that are task-agnostic, meaning that the repertoire evolution is not driven towards solving any specific task. We use a total of eight swarm robotics tasks to evaluate the behaviours contained in the evolved repertoires a-posteriori, and compare their performance with direct task-specific evolution. We show that the repertoires contain a wide diversity of swarm behaviours, and for most of the tasks, the behaviours in the repertoire have a performance close to the performance achieved by task-specific evolution.

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Notes

  1. 1.

    Preliminary experiments revealed that quality domination yielded repertoires composed of higher-quality solutions, and with a similar behaviour space coverage.

References

  1. Bayındır, L.: A review of swarm robotics tasks. Neurocomputing 172, 292–321 (2016)

    Article  Google Scholar 

  2. Beck, A., Sabach, S.: Weiszfeld’s method: old and new results. J. Optim. Theory Appl. 164(1), 1–40 (2015)

    Article  MathSciNet  Google Scholar 

  3. Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)

    Article  Google Scholar 

  4. Brown, D.S., Turner, R., Hennigh, O., Loscalzo, S.: Discovery and exploration of novel swarm behaviors given limited robot capabilities. In: Groß, R., et al. (eds.) Distributed Autonomous Robotic Systems. SPAR, vol. 6, pp. 447–460. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73008-0_31

    Chapter  Google Scholar 

  5. Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_2

    Chapter  Google Scholar 

  6. Cully, A., Clune, J., Tarapore, D., Mouret, J.B.: Robots that can adapt like animals. Nature 521(7553), 503–507 (2015)

    Article  Google Scholar 

  7. Cully, A., Demiris, Y.: Quality and diversity optimization: a unifying modular framework. IEEE Trans. Evol. Comput. 22(2), 245–259 (2017)

    Article  Google Scholar 

  8. Cully, A., Mouret, J.B.: Evolving a behavioral repertoire for a walking robot. Evol. Comput. 24(1), 59–88 (2016)

    Article  Google Scholar 

  9. Doncieux, S., Bredeche, N., Mouret, J.B., Eiben, A.E.G.: Evolutionary robotics: what, why, and where to. Front. Robot. AI 2, 4 (2015)

    Article  Google Scholar 

  10. Doncieux, S., Mouret, J.B.: Behavioral diversity measures for evolutionary robotics. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE Press (2010)

    Google Scholar 

  11. Doncieux, S., Mouret, J.B.: Beyond black-box optimization: a review of selective pressures for evolutionary robotics. Evol. Intell. 7(2), 71–93 (2014)

    Article  Google Scholar 

  12. Duarte, M., et al.: Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE 11(3), e0151834 (2016)

    Article  MathSciNet  Google Scholar 

  13. Duarte, M., Gomes, J., Costa, V., Oliveira, S.M., Christensen, A.L.: Hybrid control for a real swarm robotics system in an intruder detection task. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9598, pp. 213–230. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31153-1_15

    Chapter  Google Scholar 

  14. Duarte, M., Gomes, J., Oliveira, S.M., Christensen, A.L.: EvoRBC: evolutionary repertoire-based control for robots with arbitrary locomotion complexity. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 93–100. ACM Press (2016)

    Google Scholar 

  15. Duarte, M., Gomes, J., Oliveira, S.M., Christensen, A.L.: Evolution of repertoire-based control for robots with complex locomotor systems. IEEE Trans. Evol. Comput. 22(2), 314–328 (2018)

    Article  Google Scholar 

  16. Duarte, M., Oliveira, S.M., Christensen, A.L.: Evolution of hierarchical controllers for multirobot systems. In: International Conference on the Synthesis & Simulation of Living Systems, pp. 657–664. MIT Press (2014)

    Google Scholar 

  17. Engebråten, S.A., Moen, J., Yakimenko, O., Glette, K.: Evolving a repertoire of controllers for a multi-function swarm. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 734–749. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_49

    Chapter  Google Scholar 

  18. Francesca, G., et al.: AutoMoDe-Chocolate: automatic design of control software for robot swarms. Swarm Intell. 9(2–3), 125–152 (2015)

    Article  Google Scholar 

  19. Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., Birattari, M.: AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8(2), 89–112 (2014)

    Article  Google Scholar 

  20. Gauci, M., Chen, J., Li, W., Dodd, T.J., Gross, R.: Clustering objects with robots that do not compute. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 421–428. International Foundation for Autonomous Agents and Multiagent Systems (2014)

    Google Scholar 

  21. Gauci, M., Chen, J., Li, W., Dodd, T.J., Groß, R.: Self-organized aggregation without computation. Int. J. Robot. Res. 33(8), 1145–1161 (2014)

    Article  Google Scholar 

  22. Gomes, J., Christensen, A.L.: Generic behaviour similarity measures for evolutionary swarm robotics. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 199–206. ACM Press (2013)

    Google Scholar 

  23. Gomes, J., Christensen, A.L.: Comparing approaches for evolving high-level robot control based on behaviour repertoires. In: IEEE Congress on Evolutionary Computation (2018). (in Press)

    Google Scholar 

  24. Gomes, J., Mariano, P., Christensen, A.L.: Systematic derivation of behaviour characterisations in evolutionary robotics. In: International Conference on the Synthesis and Simulation of Living Systems (ALife), pp. 212–219. MIT Press (2014)

    Google Scholar 

  25. Gomes, J., Mariano, P., Christensen, A.L.: Devising effective novelty search algorithms: a comprehensive empirical study. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 943–950. ACM Press (2015)

    Google Scholar 

  26. Gomes, J., Oliveira, S.M., Christensen, A.L.: An approach to evolve and exploit repertoires of general robot behaviours. Swarm Evol. Comput. (2018). https://doi.org/10.1016/j.swevo.2018.06.009. (in Press)

  27. Gomes, J., Urbano, P., Christensen, A.L.: Evolution of swarm robotics systems with novelty search. Swarm Intell. 7(2–3), 115–144 (2013)

    Article  Google Scholar 

  28. Hamann, H.: Swarm Robotics: A Formal Approach. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-74528-2

    Book  Google Scholar 

  29. Hubert, M., Rousseeuw, P.J., Vanden Branden, K.: ROBPCA: a new approach to robust principal component analysis. Technometrics 47(1), 64–79 (2005)

    Article  MathSciNet  Google Scholar 

  30. Kim, S., Doncieux, S.: Learning highly diverse robot throwing movements through quality diversity search. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO), pp. 1177–1178. ACM Press (2017)

    Google Scholar 

  31. Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)

    Article  Google Scholar 

  32. Lehman, J., Stanley, K.O.: Evolving a diversity of virtual creatures through novelty search and local competition. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 211–218. ACM Press (2011)

    Google Scholar 

  33. Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: a multiagent simulation environment. Simulation 81(7), 517–527 (2005)

    Article  Google Scholar 

  34. Mouret, J., Clune, J.: Illuminating search spaces by mapping elites. CoRR abs/1504.04909 (2015). http://arxiv.org/abs/1504.04909

  35. Mouret, J.B., Doncieux, S.: Encouraging behavioral diversity in evolutionary robotics: an empirical study. Evol. Comput. 20(1), 91–133 (2012)

    Article  Google Scholar 

  36. Nordmoen, J., Ellefsen, K.O., Glette, K.: Combining MAP-elites and incremental evolution to generate gaits for a mammalian quadruped robot. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 719–733. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_48

    Chapter  Google Scholar 

  37. Pugh, J.K., Soros, L.B., Stanley, K.O.: Quality diversity: a new frontier for evolutionary computation. Front. Robot. AI 3, 1–40 (2016)

    Article  Google Scholar 

  38. Trianni, V.: Evolutionary Swarm Robotics: Evolving Self-Organising Behaviours in Groups of Autonomous Robots, Studies in Computational Intelligence, vol. 108. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-77612-3

    Book  Google Scholar 

  39. Trianni, V., Nolfi, S., Dorigo, M.: Evolution, self-organization and swarm robotics. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence. Natural Computing Series, pp. 163–191. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-74089-6_5

    Chapter  Google Scholar 

  40. Vassiliades, V., Chatzilygeroudis, K., Mouret, J.B.: Using centroidal Voronoi tessellations to scale up the multi-dimensional archive of phenotypic elites algorithm. IEEE Trans. Evol. Comput. (2017). https://doi.org/10.1109/TEVC.2017.2735550. (in Press)

    Article  Google Scholar 

  41. Vassiliades, V., Chatzilygeroudis, K., Mouret, J.B.: Comparing multimodal optimization and illumination. In: Genetic and Evolutionary Computation Conference (GECCO) Companion, pp. 97–98. ACM Press (2017)

    Google Scholar 

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Acknowledgments

Work supported by Fundação para a Ciência e a Tecnologia (FCT), Portugal, with grants UID/MULTI/04046/2013 (BioISI), and UID/EEA/50008/2013 (Instituto de Telecomunicações). This work used the EGI infrastructure with the support of NCG-INGRID-PT (Portugal).

Author information

Authors and Affiliations

  1. BioISI, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal

    Jorge Gomes

  2. BioMachines Lab, Lisbon, Portugal

    Jorge Gomes & Anders Lyhne Christensen

  3. Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal

    Anders Lyhne Christensen

  4. Instituto de Telecomunicações, Lisbon, Portugal

    Anders Lyhne Christensen

  5. Maersk McKinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Anders Lyhne Christensen

  6. Sonodot Ltd., London, UK

    Jorge Gomes

Authors
  1. Jorge Gomes
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  2. Anders Lyhne Christensen
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Corresponding author

Correspondence to Jorge Gomes .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Gomes, J., Christensen, A.L. (2018). Task-Agnostic Evolution of Diverse Repertoires of Swarm Behaviours. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_18

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