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A disaster response system based on human-agent collectives

Ramchurn, Sarvapali D.; Huynh, Trung Dong; Wu, Feng; Ikuno, Yuki; Flann, Jack; Moreau, Luc; Fischer, Joel E.; Jiang, Wenchao; Rodden, Tom; Simpson, Edwin; Reece, Steven; Roberts, Stephen; Jennings, Nicholas R.


Sarvapali D. Ramchurn

Trung Dong Huynh

Feng Wu

Yuki Ikuno

Jack Flann

Luc Moreau

Wenchao Jiang

Professor of Computer Science

Edwin Simpson

Steven Reece

Stephen Roberts

Nicholas R. Jennings


Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in the most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.


Ramchurn, S. D., Huynh, T. D., Wu, F., Ikuno, Y., Flann, J., Moreau, L., …Jennings, N. R. (2016). A disaster response system based on human-agent collectives. Journal of Artificial Intelligence Research, 57,

Journal Article Type Article
Acceptance Date Dec 10, 2016
Publication Date Dec 27, 2016
Deposit Date Apr 12, 2017
Publicly Available Date Apr 12, 2017
Journal Journal of Artificial Intelligence Research
Print ISSN 1076-9757
Electronic ISSN 1943-5037
Publisher AI Access Foundation
Peer Reviewed Peer Reviewed
Volume 57
Public URL
Publisher URL
Related Public URLs
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information c2016 AI Access Foundation


ramchurn_5098_hac-er_opt.pdf (1.4 Mb)

Copyright Statement
Copyright information regarding this work can be found at the following address:

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