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Human-agent collaboration for disaster response

Human-agent collaboration for disaster response
Human-agent collaboration for disaster response
In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.
human-agent interaction, human-agent collectives, disaster response
1387-2532
82-111
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Wu, Feng
b79f9800-2819-40c8-96e7-3ad85f866f5e
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Reece, Steven
b79cac5b-bbd2-4038-b47d-3d4c845802aa
Jiang, Wenchao
c93f05be-0fe0-4f1f-b8d6-326001d8edb0
Roberts, Stephen J.
4fb70865-53c6-4054-9e08-b6c566454941
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Wu, Feng
b79f9800-2819-40c8-96e7-3ad85f866f5e
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Reece, Steven
b79cac5b-bbd2-4038-b47d-3d4c845802aa
Jiang, Wenchao
c93f05be-0fe0-4f1f-b8d6-326001d8edb0
Roberts, Stephen J.
4fb70865-53c6-4054-9e08-b6c566454941
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Ramchurn, Sarvapali, Wu, Feng, Fischer, Joel, Reece, Steven, Jiang, Wenchao, Roberts, Stephen J., Rodden, Tom and Jennings, Nicholas R. (2016) Human-agent collaboration for disaster response. Autonomous Agents and Multi-Agent Systems, 30 (1), 82-111.

Record type: Article

Abstract

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.

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More information

e-pub ahead of print date: 20 February 2015
Published date: 2016
Venue - Dates: conference; 2015-01-01, 2015-01-01
Keywords: human-agent interaction, human-agent collectives, disaster response
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 374063
URI: https://eprints.soton.ac.uk/id/eprint/374063
ISSN: 1387-2532
PURE UUID: e7f95359-bac5-4cc6-85a4-2fef50a1bc56
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 04 Feb 2015 16:37
Last modified: 06 Jun 2018 12:42

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Contributors

Author: Sarvapali Ramchurn ORCID iD
Author: Feng Wu
Author: Joel Fischer
Author: Steven Reece
Author: Wenchao Jiang
Author: Stephen J. Roberts
Author: Tom Rodden
Author: Nicholas R. Jennings

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