Learning from Evolution: Improving Collective Decision-Making Mechanisms using Insights from Evolutionary Robotics
<|reference_start|>Learning from Evolution: Improving Collective Decision-Making Mechanisms using Insights from Evolutionary Robotics: Collective decision-making enables multi-robot systems to act autonomously in real-world environments. Existing collective decision-making mechanisms suffer from the so-called speed versus accuracy trade-off or rely on high complexity, e.g., by including global communication. Recent work has shown that more efficient collective decision-making mechanisms based on artificial neural networks can be generated using methods from evolutionary computation. A major drawback of these decision-making neural networks is their limited interpretability. Analyzing evolved decision-making mechanisms can help us improve the efficiency of hand-coded decision-making mechanisms while maintaining a higher interpretability. In this paper, we analyze evolved collective decision-making mechanisms in detail and hand-code two new decision-making mechanisms based on the insights gained. In benchmark experiments, we show that the newly implemented collective decision-making mechanisms are more efficient than the state-of-the-art collective decision-making mechanisms voter model and majority rule.<|reference_end|>
@article{kaiser2024learning,
    title={Learning from Evolution: Improving Collective Decision-Making Mechanisms
  using Insights from Evolutionary Robotics},
    author={Tanja Katharina Kaiser},
    journal={arXiv preprint arXiv:2405.02133},
    year={2024},
    doi={10.1145/3638529.3653988},
    archivePrefix={arXiv},
    eprint={2405.02133},
    primaryClass={cs.MA cs.NE cs.RO}
}