Our paper has been awarded the Best Paper Award of the Innovative Applications track at AAMAS.
Download the PDF here.
Abstract
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency 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 ex- tract situational awareness information from large streams of re- ports 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 pro- totype 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.
Reference
Sarvapali Ramchurn, Edwin Simpson, Joel Fischer, Trung Dong Huynh, Yuki Ikuno, Steven Reece, Wenchao Jiang, Feng Wu, Jack Flann, SJ Roberts, Luc Moreau, T Rodden, NR Jennings (2015). HAC-ER: A disaster response system based on human-agent collectives. To appear in: 4th International Conference on Autonomous Agents and Multi-Agent Systems, Istanbul, TR, 04 – 08 May 2015.