提出观察者依赖的框架，厘清机器人集群中涌现与群体的主观性。
Classifying Emergence in Robot Swarms: An Observer-Dependent Approach
- 区分可观测状态与不可观测本质，建立统一讨论基础。
- 指出群体行为本身不定义集群，生成过程才是关键。
- 适合设计与评估真实机器人集群系统的研究人员参考。
涌现与集群是广泛讨论的话题，但缺乏公认的正式定义。这一分歧不仅使新研究者难以理解，也导致专家对相同术语有不同解读。尽管已有研究尝试客观定义‘集群’或‘涌现’，并强调外部观察者的作用，但仍有学者认为，一旦观察者的视角（如范围、分辨率、上下文）确定，这些概念可被客观化或量化。本文提出一个严格框架，通过分离外部可观测状态与潜在不可观测状态，实现对现有集群与涌现定义的比较与对照。我们认为，这些概念本质上是主观的，更多由观察者的感知和隐性知识决定，而非系统本身。具体而言，‘集群’并非仅由群体行为定义，而取决于行为生成过程。我们的目标是支持机器人集群系统的设计与部署，突出多机器人系统与真正集群之间的关键差异。
原文摘要 · Abstract (English)
Emergence and swarms are widely discussed topics, yet no consensus exists on their formal definitions. This lack of agreement makes it difficult not only for new researchers to grasp these concepts, but also for experts who may use the same terms to mean different things. Many attempts have been made to objectively define 'swarm' or 'emergence,' with recent work highlighting the role of the external observer. Still, several researchers argue that once an observer's vantage point (e.g., scope, resolution, context) is established, the terms can be made objective or measured quantitatively. In this note, we propose a framework to discuss these ideas rigorously by separating externally observable states from latent, unobservable ones. This allows us to compare and contrast existing definitions of swarms and emergence on common ground. We argue that these concepts are ultimately subjective-shaped less by the system itself than by the perception and tacit knowledge of the observer. Specifically, we suggest that a 'swarm' is not defined by its group behavior alone, but by the process generating that behavior. Our broader goal is to support the design and deployment of robotic swarm systems, highlighting the critical distinction between multi-robot systems and true swarms.
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