A Disaster Response System based on Human-Agent Collectives

TY - JOUR

T1 - A Disaster Response System based on Human-Agent Collectives

AU - Ramchurn, Sarvapali

AU - Simpson, Edwin

AU - Fischer, Joel

AU - Huynh, Trung Dong

AU - Ikuno, Yuki

AU - Reece, Steven

AU - Jiang, Wenchao

AU - Wu, Feng

AU - Flann, Jack

AU - Roberts, Stephen J.

AU - Moreau, Luc

AU - Rodden, T.

AU - Jennings, N.R.

PY - 2016/12

Y1 - 2016/12

N2 - Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose sig- nificant 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 popu- lation and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team has the best available capabilities to perform tasks successfully as they navigate a space that may have significantly changed in structure from its pre-disaster state. Third, given the dynamic nature of a disaster space, and the uncertainties in- volved in performing rescue missions, information about the disaster space and the actors within it needs to be stored and verified 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 that addresses some of the situational awareness and coordination challenges faced by emergency responders in real-world disaster environments. Informed by focus groups with domain experts and real-world trials with volunteers from a number of organisations, HAC- ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. 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 gather most important sit- uational 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 a provenance infrastructure for tracking the provenance of information shared across the entire system to ensure its accountability. A Provenance Agent was also developed to monitor recorded provenance data to help stakeholders in the system to react to changes during an operation in a timely manner. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art tech- niques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.

AB - Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose sig- nificant 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 popu- lation and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team has the best available capabilities to perform tasks successfully as they navigate a space that may have significantly changed in structure from its pre-disaster state. Third, given the dynamic nature of a disaster space, and the uncertainties in- volved in performing rescue missions, information about the disaster space and the actors within it needs to be stored and verified 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 that addresses some of the situational awareness and coordination challenges faced by emergency responders in real-world disaster environments. Informed by focus groups with domain experts and real-world trials with volunteers from a number of organisations, HAC- ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. 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 gather most important sit- uational 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 a provenance infrastructure for tracking the provenance of information shared across the entire system to ensure its accountability. A Provenance Agent was also developed to monitor recorded provenance data to help stakeholders in the system to react to changes during an operation in a timely manner. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art tech- niques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.

U2 - 10.1613/jair.5098

DO - 10.1613/jair.5098

M3 - Article

SN - 1076-9757

VL - 57

SP - 661

EP - 708

JO - Journal Artificial Intelligence Research

JF - Journal Artificial Intelligence Research

ER -