论文标题
上下文意识到异质群的智能控制剂
Contextually Aware Intelligent Control Agents for Heterogeneous Swarms
论文作者
论文摘要
群蜂群研究中的一个新兴挑战是设计有效，有效的人工智能算法，以保持低计算的上限，同时提高群体在各种情况下运作的能力。我们提出了一种设计一种方法来设计上下文感知的群体控制智能代理。智能控制代理（Shepherd）首先使用群指标来识别与该特定群体类型的行为库中选择合适的参数化的群体类型。我们方法论的设计原则是提高控制代理的情况意识（即信息内容），而无需牺牲有效的群体控制所需的低计算成本。我们展示了同质和异质群中成功的牧羊。
An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain a low-computational ceiling while increasing the swarm's abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm-control intelligent agent. The intelligent control agent (shepherd) first uses swarm metrics to recognise the type of swarm it interacts with to then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. information contents) of the control agent without sacrificing the low-computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.
