Abstract
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.
Abstract (translated)
集体决策使多机器人系统能够自主地在现实环境中行动。现有的集体决策机制存在所谓的速度与精度权衡，或者依赖于高复杂性，例如通过包括全局通信。最近的工作表明，基于人工神经网络的更高效集体决策机制可以通过进化计算方法来生成。这些决策神经网络的一个主要缺点是它们的可解释性有限。分析进化的决策机制可以帮助我们提高手编决策机制的效率，同时保持更高的可解释性。在本文中，我们详细分析了进化的集体决策机制，并基于所获得的见解手编了两个新的决策机制。在基准实验中，我们证明了新实施的共同决策机制比最先进的共同决策选民模型和多数规则更加高效。
URL
https://arxiv.org/abs/2405.02133
