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Evolution of Signaling in a Multi-Robot System: Categorization and Communication

机译：多机器人系统中信号的演变：分类和通信

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Communication is of central importance in collective robotics, as it is integral to the switch from solitary to social behavior. In this article, we study emergent communication behaviors that are not predetermined by the experimenter, but are shaped by artificial evolution, together with the rest of the behavioral repertoire of the robots. In particular, we describe a set of experiments in which artificial evolution is used as a means to engineer robot neuro-controllers capable of guiding groups of robots in a categorization task by producing appropriate actions. The categorization is a result of how robots' sensory inputs unfold in time, and, more specifically, of the integration over time of sensory input. In spite of the absence of explicit selective pressure (coded into the fitness function), which would favor signaling over non-signaling groups, communicative behavior emerges. Post-evaluation analyses illustrate the adaptive function of the evolved signals and show that these signals are tightly linked to the behavioral repertoire of the agents. Signals evolve because communication enhances group performance, revealing a "hidden" benefit for social behavior. This benefit is related to obtaining robust and fast decision-making mechanisms. More generally, we show how processes requiring the categorization of noisy dynamical information might be improved by social interactions mediated by communication. In a further series of experiments, we successfully download evolved controllers onto real s-bots. We discuss the challenges involved in porting neuro-controllers displaying time-based decision-making processes onto real robots. Finally, the beneficial effect of communication is shown to transfer to the case of a real robot, and the robustness of the behavior against inter-robot differences is discussed.
机译：在集体机器人中，交流是至关重要的，因为交流是从孤独行为向社会行为转变的组成部分。在本文中，我们研究了并非由实验人员预先确定但由人为进化塑造的紧急通信行为，以及机器人的其余行为方式。特别是，我们描述了一组实验，其中人工进化被用作设计机器人神经控制器的方法，该控制器能够通过产生适当的动作来指导分类任务中的机器人组。归类是机器人的感觉输入如何及时展开的结果，更具体地讲，是随着感觉输入随时间的推移进行整合的结果。尽管没有显式的选择压力（编码到适应度函数中）（比无信号的组更倾向于发信号），但还是出现了交流行为。评估后分析说明了进化信号的自适应功能，并表明这些信号与代理的行为组成紧密相关。信号不断发展，因为沟通可以提高团队绩效，从而揭示社交行为的“隐藏”优势。此好处与获得强大而快速的决策机制有关。更广泛地说，我们展示了需要通过通信介导的社交互动来改善需要对嘈杂的动态信息进行分类的过程。在进一步的实验中，我们成功地将进化的控制器下载到真实的S-bot上。我们讨论了将显示基于时间的决策过程的神经控制器移植到实际机器人中所涉及的挑战。最后，显示了通信的有益效果可以转移到真实机器人的案例中，并且讨论了针对机器人之间差异的行为的鲁棒性。

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