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Human–agent collaboration for disaster response

Ramchurn, Sarvapali D.; Wu, Feng; Jiang, Wenchao; Fischer, Joel E.; Reece, Steve; Roberts, Stephen; Rodden, Tom; Greenhalgh, Chris; Jennings, Nicholas R.

Authors

Sarvapali D. Ramchurn sdr@ecs.soton.ac.uk

Feng Wu

Wenchao Jiang

Steve Reece

Stephen Roberts

TOM RODDEN TOM.RODDEN@NOTTINGHAM.AC.UK
Professor of Computer Science

Nicholas R. Jennings



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.

Citation

Ramchurn, S. D., Wu, F., Jiang, W., Fischer, J. E., Reece, S., Roberts, S., …Jennings, N. R. (2016). Human–agent collaboration for disaster response. Autonomous Agents and Multi-Agent Systems, 30(1), 82-111. https://doi.org/10.1007/s10458-015-9286-4

Journal Article Type Article
Online Publication Date Feb 20, 2015
Publication Date 2016-01
Deposit Date Jan 29, 2016
Publicly Available Date Jan 29, 2016
Journal Autonomous Agents and Multi-Agent Systems
Print ISSN 1387-2532
Electronic ISSN 1573-7454
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 30
Issue 1
Pages 82-111
DOI https://doi.org/10.1007/s10458-015-9286-4
Keywords Human-agent interaction, Human-agent collectives, Disaster response
Public URL http://eprints.nottingham.ac.uk/id/eprint/31396
Publisher URL http://link.springer.com/article/10.1007/s10458-015-9286-4
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-015-9286-4

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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