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.

翻译：集体行为通常需要通过数值特征来表达，例如用于分类或模仿学习。针对特定群体行为场景，研究者常提出临时特征集来解决该问题，但通常未进一步考虑解决方案在预设场景之外的鲁棒性。然而，自动设计群体行为方法的发展依赖于定量测量群体行为相似性的能力。因此，我们研究了特征集对集体行为的影响。我们从先前主要考虑狭窄行为场景的群体机器人研究中选取群体特征集与相似性度量方法，并评估其稳健性。我们证明特征集与相似性度量的相互作用使得某些组合更适合区分相似行为组。此外，我们提出一种基于自组织映射的方法，用于识别特征空间中行为难以区分的区域。