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@ARTICLE{JimenezRomero:1019523,
      author       = {Jimenez-Romero, Cristian and Yegenoglu, Alper and Pérez
                      Martín, Aarón and Diaz, Sandra and Morrison, Abigail},
      title        = {{E}mergent communication enhances foraging behavior in
                      evolved swarms controlled by spiking neural networks},
      journal      = {Swarm intelligence},
      volume       = {18},
      issn         = {1935-3812},
      address      = {New York, NY [u.a.]},
      publisher    = {Springer},
      reportid     = {FZJ-2023-05470},
      pages        = {1-28},
      year         = {2024},
      abstract     = {Social insects such as ants and termites communicate via
                      pheromones which allow them to coordinate their activity and
                      solve complex tasks as a swarm, e.g. foraging for food or
                      finding their way back to the nest. This behavior was shaped
                      through evolutionary processes over millions of years. In
                      computational models, self-coordination in swarms has been
                      implemented using probabilistic or pre-defined simple action
                      rules to shape the decision of each agent and the collective
                      behavior. However, manual tuned decision rules may limit the
                      emergent behavior of the swarm. In this work we investigate
                      the emergence of self-coordination and communication in
                      evolved swarms without defining any explicit rule. For this
                      purpose, we evolve a swarm of agents representing an ant
                      colony. We use an evolutionary algorithm to optimize a
                      spiking neural network (SNN) which serves as an artificial
                      brain to control the behavior of each agent. The goal of the
                      evolved colony is to find optimal ways to forage for food
                      and return it to the nest in the shortest amount of time. In
                      the evolutionary phase, the ants are able to learn to
                      collaborate by depositing pheromone near food piles and near
                      the nest to guide other ants. The pheromone usage is not
                      manually encoded into the network; instead, this behavior is
                      established through the optimization procedure. We observe
                      that pheromone-based communication enables the ants to
                      perform better in comparison to colonies where communication
                      via pheromone did not emerge. Furthermore, we assess the
                      foraging performance of the ant colonies by comparing the
                      SNN-based model to a multi-agent rule-based system. Our
                      results show that the SNN-based model can efficiently
                      complete the foraging task in a short amount of time. Our
                      approach illustrates that even in the absence of pre-defined
                      rules, self-coordination via pheromone emerges as a result
                      of the network optimization. This work serves as a proof of
                      concept for the possibility of creating complex applications
                      utilizing SNNs as underlying architectures for multiagent
                      interactions where communication and self-coordination is
                      desired.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HBP SGA3 - Human
                      Brain Project Specific Grant Agreement 3 (945539) / ICEI -
                      Interactive Computing E-Infrastructure for the Human Brain
                      Project (800858) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / HDS LEE -
                      Helmholtz School for Data Science in Life, Earth and Energy
                      (HDS LEE) (HDS-LEE-20190612) / CSD-SSD - Center for
                      Simulation and Data Science (CSD) - School for Simulation
                      and Data Science (SSD) (CSD-SSD-20190612) / PhD no Grant -
                      Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)
                      / SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)945539 /
                      G:(EU-Grant)800858 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(DE-Juel1)HDS-LEE-20190612 / G:(DE-Juel1)CSD-SSD-20190612
                      / G:(DE-Juel1)PHD-NO-GRANT-20170405 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001122634800001},
      doi          = {10.1007/s11721-023-00231-6},
      url          = {https://juser.fz-juelich.de/record/1019523},
}