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 ha...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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Springer
2016
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| Online Access: | https://eprints.nottingham.ac.uk/31396/ |
| _version_ | 1848794192803790848 |
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| author | Ramchurn, Sarvapali D. Wu, Feng Jiang, Wenchao Fischer, Joel E. Reece, Steve Roberts, Stephen Rodden, Tom Greenhalgh, Chris Jennings, Nicholas R. |
| author_facet | Ramchurn, Sarvapali D. Wu, Feng Jiang, Wenchao Fischer, Joel E. Reece, Steve Roberts, Stephen Rodden, Tom Greenhalgh, Chris Jennings, Nicholas R. |
| author_sort | Ramchurn, Sarvapali D. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:12:17Z |
| format | Article |
| id | nottingham-31396 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:17Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-313962020-05-04T17:31:59Z https://eprints.nottingham.ac.uk/31396/ 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. 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. Springer 2016-01-15 Article PeerReviewed Ramchurn, Sarvapali D., Wu, Feng, Jiang, Wenchao, Fischer, Joel E., Reece, Steve, Roberts, Stephen, Rodden, Tom, Greenhalgh, Chris and Jennings, Nicholas R. (2016) Human–agent collaboration for disaster response. Autonomous Agents and Multi-Agent Systems, 30 (1). pp. 82-111. ISSN 1573-7454 Human-agent interaction Human-agent collectives Disaster response http://link.springer.com/article/10.1007/s10458-015-9286-4 doi:10.1007/s10458-015-9286-4 doi:10.1007/s10458-015-9286-4 |
| spellingShingle | Human-agent interaction Human-agent collectives Disaster response Ramchurn, Sarvapali D. Wu, Feng Jiang, Wenchao Fischer, Joel E. Reece, Steve Roberts, Stephen Rodden, Tom Greenhalgh, Chris Jennings, Nicholas R. Human–agent collaboration for disaster response |
| title | Human–agent collaboration for disaster response |
| title_full | Human–agent collaboration for disaster response |
| title_fullStr | Human–agent collaboration for disaster response |
| title_full_unstemmed | Human–agent collaboration for disaster response |
| title_short | Human–agent collaboration for disaster response |
| title_sort | human–agent collaboration for disaster response |
| topic | Human-agent interaction Human-agent collectives Disaster response |
| url | https://eprints.nottingham.ac.uk/31396/ https://eprints.nottingham.ac.uk/31396/ https://eprints.nottingham.ac.uk/31396/ |