Autonomous robot swarms must be able to make fast and accurate collective decisions, but speed and accuracy are known to be conflicting goals. While collective decision-making is widely studied in swarm robotics research, only few works on using methods of evolutionary computation to generate collective decision-making mechanisms exist. These works use task-specific fitness functions rewarding the accomplishment of the respective collective decision-making task. But task-independent rewards, such as for prediction error minimization, may promote the emergence of diverse and innovative solutions. We evolve collective decision-making mechanisms using a task-specific fitness function rewarding correct robot opinions, a task-independent reward for prediction accuracy, and a hybrid fitness function combining the two previous. In our simulations, we use the collective perception scenario, that is, robots must collectively determine which of two environmental features is more frequent. We show that evolution successfully optimizes fitness in all three scenarios, but that only the task-specific fitness function and the hybrid fitness function lead to the emergence of collective decision-making behaviors. In benchmark experiments, we show the competitiveness of the evolved decision-making mechanisms to the voter model and the majority rule and analyze the scalability of the decision-making mechanisms with problem difficulty.

翻译：自主机器人群体必须能够快速且准确地做出集体决策，但速度与准确性被认为是相互冲突的目标。虽然集体决策在群体机器人研究中已被广泛探讨，但利用进化计算方法生成集体决策机制的相关工作极少。现有工作通常采用任务特定适应度函数，通过奖励完成相应集体决策任务来优化机制。然而，任务无关奖励（如预测误差最小化）可能促进多样化与创新性解决方案的出现。本研究分别采用奖励机器人意见准确性的任务特定适应度函数、奖励预测精度的任务无关奖励，以及融合前两者的混合适应度函数，来演化集体决策机制。在仿真实验中，我们使用集体感知场景（即机器人需集体判定两种环境特征中哪种更常见）。结果表明，进化过程在三类场景下均成功优化了适应度，但仅任务特定适应度函数与混合适应度函数能催生集体决策行为。通过基准测试，我们证明演化出的决策机制在投票者模型与多数规则下具有竞争力，并分析了决策机制随问题难度的可扩展性。