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Outlining the Design Space of eXplainable Swarm (xSwarm): Experts’ Perspective.

Naiseh, M., Soorati, M. D. and Ramchurn, S., 2024. Outlining the Design Space of eXplainable Swarm (xSwarm): Experts’ Perspective. In: Bourgeois, J., Paik, J., Piranda, B., Werfel, J., Hauert, S., Pierson, A., Hamann, H., Lam, T-L, Matsuno, F., Mehr, N. and Makhoul, A., eds. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics. Cham: Springer, 28-41.

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DOI: 10.1007/978-3-031-51497-5_3


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 improves 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 discussion on xSwarm and paves the way for more research in the field.

Item Type:Book Section
Group:Faculty of Science & Technology
ID Code:39778
Deposited By: Symplectic RT2
Deposited On:02 May 2024 12:08
Last Modified:02 May 2024 12:08


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