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Autonomous Agents and Multi-Agent Systems
Erschienen in: Autonomous Agents and Multi-Agent Systems 1/2016

01.01.2016

Human–agent collaboration for disaster response

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

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 1/2016

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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.
Vorheriger Artikel NegoChat-A: a chat-based negotiation agent with bounded rationality
Nächster Artikel Looking for Conflict: Gaze Dynamics in a Dyadic Mixed-Motive Game

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4
As access to emergency responders is either limited or costly for field trials, it was considered reasonable to hire volunteers that were taught to use the tools we gave them. The design of a fully-fledged training tool for disaster responder would be beyond the scope of this paper.
 
5
Given the invisibility of radiation, it is possible to create a believable and challenging environment for the responders to solve in our mixed-reality game (see Sect. 5).
 
6
This assumption is not central to our problem and only serves to inform the decision making of the agent as we see later. It is also possible to obtain similar information about radiation levels by fusing the responders’ geiger counter readings, but this is beyond the scope of the paper.
 
7
While some agencies may be trained to obey orders (e.g., military or fire-fighting), others (e.g., transport providers or medics) are not always trained to do so [23].
 
8
Other methods such as sequential greedy assignment or swap-based hill climbing [42] may also be useful. However, they do not explore the policy space as well as MCTS [29].
 
11
The EKF accommodates the nonlinearities in the radiation dynamics expressed through Eq. (4).
 
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Metadaten
Titel
Human–agent collaboration for disaster response
verfasst von
Sarvapali D. Ramchurn
Feng Wu
Wenchao Jiang
Joel E. Fischer
Steve Reece
Stephen Roberts
Tom Rodden
Chris Greenhalgh
Nicholas R. Jennings
Publikationsdatum
01.01.2016
Verlag
Springer US
Erschienen in
Autonomous Agents and Multi-Agent Systems / Ausgabe 1/2016
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI
https://doi.org/10.1007/s10458-015-9286-4

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