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.

翻译：集体决策使多机器人系统能够在现实环境中自主行动。现有集体决策机制存在所谓的速度与准确性权衡问题，或依赖高复杂度（例如引入全局通信）。近期研究表明，基于人工神经网络的更高效率集体决策机制可通过进化计算方法生成。这类决策神经网络的主要缺陷在于其可解释性有限。分析进化决策机制有助于我们提升人工编码决策机制的效率，同时保持较高的可解释性。本文详细分析了进化集体决策机制，并基于获得的洞见手工设计了两种新决策机制。基准实验表明，新实现的集体决策机制比现有最先进的集体决策机制——投票者模型和多数规则——具有更高效率。