In swarm robotics, agents interact through local roles to solve complex tasks beyond an individual's ability. Even though swarms are capable of carrying out some operations without the need for human intervention, many safety-critical applications still call for human operators to control and monitor the swarm. There are novel challenges to effective Human-Swarm Interaction (HSI) that are only beginning to be addressed. Explainability is one factor that can facilitate effective and trustworthy HSI and improve the overall performance of Human-Swarm team. Explainability was studied across various Human-AI domains, such as Human-Robot Interaction and Human-Centered ML. However, it is still ambiguous whether explanations studied in Human-AI literature would be beneficial in Human-Swarm research and development. Furthermore, the literature lacks foundational research on the prerequisites for explainability requirements in swarm robotics, i.e., what kind of questions an explainable swarm is expected to answer, and what types of explanations a swarm is expected to generate. By surveying 26 swarm experts, we seek to answer these questions and identify challenges experts faced to generate explanations in Human-Swarm environments. Our work contributes insights into defining a new area of research of eXplainable Swarm (xSwarm) which looks at how explainability can be implemented and developed in swarm systems. This paper opens the discussion on xSwarm and paves the way for more research in the field.

翻译：在蜂群机器人学中，智能体通过局部角色交互以完成超越个体能力的复杂任务。尽管蜂群能够在无需人工干预的情况下执行某些操作，但许多安全关键应用仍要求人类操作员对蜂群进行控制与监控。有效的人-蜂群交互（HSI）面临诸多新兴挑战，而这些挑战目前仅开始被探索。可解释性是促进有效且可信的人-蜂群交互、提升人-蜂团队整体性能的关键因素之一。可解释性已在人-机器人交互、以人为中心的机器学习等不同人-人工智能领域得到研究。然而，人-人工智能文献中研究的解释方法是否同样适用于人-蜂群研究与开发仍不明确。此外，现有文献缺乏对蜂群机器人学中可解释性需求前提的基础性研究，例如：一个可解释蜂群应回答何种问题，以及蜂群应生成何种类型的解释。通过调研26位蜂群专家，我们旨在回答上述问题，并识别专家在人-蜂群环境中生成解释时所面临的挑战。我们的工作为定义可解释蜂群（xSwarm）这一新兴研究领域提供了洞见，该领域聚焦于如何在蜂群系统中实现并发展可解释性。本文开启了关于xSwarm的讨论，并为该领域的进一步研究铺平了道路。