Abstract
In this work, we develop a set of behavioral and conditional modules for the use with behavior trees. We present AutoMoDe-Cedrata, an automatic modular design method that automatically assembles and fine-tunes these modules into behavior trees that control robot swarms. We test Cedrata on three missions and, to gain further insights on its effectiveness, we design control software for the same missions using AutoMoDe-Maple, another automatic design method, and by a group of human designers. Results show that the proposed modules allow for well-performing behavior trees. Yet, Cedrata had difficulties automatically generating control software that performs similarly well as the one generated by human designers, especially when involving communication.
JK and VvP contributed equally to this work and should be considered as co-first authors. The experiments were designed by JK and VvP and performed by VvP. The paper was drafted by JK and edited by MB; all authors read and commented the final version. The research was directed by MB.
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Acknowledgements
JK and MB acknowledge support by the FNRS. The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (DEMIURGE Project, grant agreement No 681872) and from Belgium’s Wallonia-Brussels Federation through the ARC Advanced Project GbO–Guaranteed by Optimization.
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Kuckling, J., van Pelt, V., Birattari, M. (2021). Automatic Modular Design of Behavior Trees for Robot Swarms with Communication Capabilites. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_9
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