HAC-ER: A Disaster Response System based on Human-Agent Collectives

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This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emergency responders by enabling humans and agents, using state-of-the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC-ER utilises crowdsourcing combined with machine learning to extract situational awareness information 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 a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.

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 address = {Istanbul, Turkey},
 author = {Sarvapali D. Ramchurn and Trung Dong Huynh and Yuki Ikuno and Jack Flann and Feng Wu and Luc Moreau and Nicholas R. Jennings and Joel E. Fischer and Wenchao Jiang and Tom Rodden and Edwin Simpson and Steven Reece and Stephen Roberts},
 booktitle = {Proceedings of the 14th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
 month = {May},
 pages = {533-541},
 title = {HAC-ER: A Disaster Response System based on Human-Agent Collectives},
 year = {2015}