Emergency Response Management (ERM) is a critical problem faced
by communities across the globe. Despite this, it is common for ERM
systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty
have been explored but have failed to be accepted into real systems.
We identify a key issue impeding their adoption — algorithmic
approaches to emergency response focus on reactive, post-incident
dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response
dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that
the crucial period of planning for ERM systems is not post-incident,
but between incidents. This is not a trivial planning problem — a
major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal
problem in ERM systems is planning under limited communication,
which is particularly important in disaster scenarios that affect
communication networks. We address both problems by proposing
two partially decentralized multi-agent planning algorithms that
utilize heuristics and exploit the structure of the dispatch problem.
We evaluate our proposed approach using real-world data, and find
that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response
time as well as its variance.
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Designing Emergency Response Pipelines : Lessons and Challenges
Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities. Emergency response management comprises of several stages and sub-problems like forecasting, resource allocation, and dispatch. The design of principled approaches to tackle each problem is necessary to create efficient emergency response management (ERM) pipelines. Over the last six years, we have worked with several first responder organizations to design ERM pipelines. In this paper, we highlight some of the challenges that we have identified and lessons that we have learned through our experience in this domain. Such challenges are particularly relevant for practitioners and researchers, and are important considerations even in the design of response strategies to mitigate disasters like floods and earthquakes.
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- Award ID(s):
- 1814958
- NSF-PAR ID:
- 10275730
- Date Published:
- Journal Name:
- AI for Social Good Workshop, AAAI Fall Symposium Series 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption --- algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.more » « less
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Accepted Manuscript1.0
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