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A disaster response system based on human-agent collectives

A disaster response system based on human-agent collectives
A disaster response system based on human-agent collectives
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 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 involved 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 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 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 techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
661-708
Ramchurn, Sarvapali D.
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Huynh, Trung Dong
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Wu, Feng
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Ikuno, Yuki
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Flann, Jack
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Moreau, Luc
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Fischer, Joel
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Jiang, Wenchao
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Rodden, Tom
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Simpson, Edwin
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Reece, Steven
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Roberts, Stephen
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Jennings, Nicholas R.
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Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Wu, Feng
b79f9800-2819-40c8-96e7-3ad85f866f5e
Ikuno, Yuki
e5ae8aa8-f9fb-4455-8a24-30448780da15
Flann, Jack
e7c45bef-2d6b-410f-a7ab-4629f34fa086
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Jiang, Wenchao
c93f05be-0fe0-4f1f-b8d6-326001d8edb0
Rodden, Tom
8f77ed87-05f8-4117-8a56-6bed554fb222
Simpson, Edwin
ebf1cc2d-6633-4182-ab5a-91c832816a97
Reece, Steven
b79cac5b-bbd2-4038-b47d-3d4c845802aa
Roberts, Stephen
fef5d01c-92bd-44cf-93f0-923ec24f8875
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Ramchurn, Sarvapali D., Huynh, Trung Dong, Wu, Feng, Ikuno, Yuki, Flann, Jack, Moreau, Luc, Fischer, Joel, Jiang, Wenchao, Rodden, Tom, Simpson, Edwin, Reece, Steven, Roberts, Stephen and Jennings, Nicholas R. (2016) A disaster response system based on human-agent collectives. Journal of Artificial Intelligence Research, 57, 661-708. (doi:10.1613/jair.5098).

Record type: Article

Abstract

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 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 involved 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 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 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 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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ramchurn_5098_hac-er_opt - Accepted Manuscript
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Accepted/In Press date: 15 October 2016
e-pub ahead of print date: 31 December 2016
Published date: 2016
Organisations: Web & Internet Science, Electronics & Computer Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 407368
URI: http://eprints.soton.ac.uk/id/eprint/407368
PURE UUID: d8bf15cd-12ca-4dca-9865-bff7d5fae519
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Trung Dong Huynh: ORCID iD orcid.org/0000-0003-4937-2473
ORCID for Luc Moreau: ORCID iD orcid.org/0000-0002-3494-120X

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Date deposited: 04 Apr 2017 01:08
Last modified: 17 Dec 2019 01:47

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Contributors

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

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