提出新方法对比群体行为，提升自动设计的准确性。
How Swarms Differ: Challenges in Collective Behaviour Comparison
- 用自组织映射识别特征空间中难以区分的行为区域。
- 发现特征集与相似性度量组合影响行为区分效果。
- 适用于群体机器人行为设计与分类的场景。
群体行为常需通过数值特征表达，如用于分类或模仿学习。现有方法多针对特定群体行为场景设计特征集，缺乏对泛化能力的考量。而自动设计群体行为依赖于对行为相似性的定量度量。本文选取已有群体机器人研究中的特征集与相似性度量，评估其在窄范围行为情境下的鲁棒性。结果表明，特征集与相似性度量的组合显著影响行为区分能力。同时提出基于自组织映射的方法，识别特征空间中行为难以区分的区域，有助于优化行为设计与比较。
原文摘要 · Abstract (English)
Collective behaviours often need to be expressed through numerical features, e.g., for classification or imitation learning. This problem is often addressed by proposing an ad-hoc feature set for a particular swarm behaviour context, usually without further consideration of the solution's resilience outside of the conceived context. Yet, the development of automatic methods to design swarm behaviours is dependent on the ability to measure quantitatively the similarity of swarm behaviours. Hence, we investigate the impact of feature sets for collective behaviours. We select swarm feature sets and similarity measures from prior swarm robotics works, which mainly considered a narrow behavioural context and assess their robustness. We demonstrate that the interplay of feature set and similarity measure makes some combinations more suitable to distinguish groups of similar behaviours. We also propose a self-organised map-based approach to identify regions of the feature space where behaviours cannot be easily distinguished.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。