Title: Evolution of Collective Decision-Making Mechanisms for Collective Perception Show Chinese title

Title: 集体决策机制在集体感知中的演化

Abstract: 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. Show Chinese abstract

Abstract: 自主机器人集群必须能够做出快速且准确的集体决策，但速度和准确性已被证明是相互冲突的目标。 虽然集体决策在群体机器人研究中被广泛研究，但使用进化计算方法生成集体决策机制的研究却很少。 这些研究使用任务特定的适应度函数，奖励完成相应集体决策任务。 但任务无关的奖励，例如预测误差最小化，可能会促进多样化和创新性解决方案的出现。 我们使用任务特定的适应度函数奖励正确的机器人意见、任务无关的预测准确性奖励以及结合前两者的混合适应度函数来进化集体决策机制。 在我们的模拟中，我们使用集体感知场景，即机器人必须共同确定两个环境特征中哪一个更常见。 我们表明，在所有三种情况下进化成功优化了适应度，但只有任务特定的适应度函数和混合适应度函数导致了集体决策行为的出现。 在基准实验中，我们展示了进化得到的决策机制与投票模型和多数规则的竞争性，并分析了决策机制随问题难度的可扩展性。