基于枢纽的蜂群的性能预测
Performance Prediction of Hub-Based Swarms
AI总结：
针对基于枢纽群体的集体状态空间随智能体数量快速增长的问题，提出结合图编码器的图表示来学习可扩展的低维嵌入，并通过状态聚类和轨迹结构实验证明其可预测蜂群性能。
摘要：
基于枢纽的群体由多个智能体组成，它们共享一个被称为枢纽的共同巢穴。智能体在远离枢纽的地方执行任务，例如觅食或收集有关未来巢穴地点的信息。对基于枢纽的群体进行建模具有挑战性，因为随着智能体数量的增加，集体状态空间的规模会迅速增长。本文提出了一种基于图的群体表示方法，该方法可以与基于图的编码器相结合，以创建可扩展到许多智能体的集体状态低维表示，用于N选优群体问题。我们通过两个实验展示了低维嵌入中的信息如何被使用。首先，我们展示了张量中的信息如何用于针对一个非常小的问题，按照选择最佳地点的概率对集体状态进行聚类。其次，我们展示了当使用图编码器学习低维嵌入时，结构化的集体轨迹如何出现，并且这些轨迹包含可用于预测蜂群性能的信息。
英文摘要：
A hub-based colony consists of multiple agents who share a common nest site called the hub. Agents perform tasks away from the hub like foraging for food or gathering information about future nest sites. Modeling hub-based colonies is challenging because the size of the collective state space grows rapidly as the number of agents grows. This paper presents a graph-based representation of the colony that can be combined with graph-based encoders to create low-dimensional representations of collective state that can scale to many agents for a best-of-N colony problem. We demonstrate how the information in the low-dimensional embedding can be used with two experiments. First, we show how the information in the tensor can be used to cluster collective states by the probability of choosing the best site for a very small problem. Second, we show how structured collective trajectories emerge when a graph encoder is used to learn the low-dimensional embedding, and these trajectories have information that can be used to predict swarm performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
