A Disaster Response System based on Human-Agent Collectives
- Submitting institution
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University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 224991001
- Type
- D - Journal article
- DOI
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10.1613/jair.5098
- Title of journal
- Journal of Artificial Intelligence Research
- Article number
- -
- First page
- 661
- Volume
- 57 (2016)
- Issue
- -
- ISSN
- 1076-9757
- Open access status
- Deposit exception
- Month of publication
- December
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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12
- Research group(s)
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A - Artificial Intelligence and Autonomy
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work integrates HCI, machine learning, multi-agent systems and data provenance for disaster response (awarded Best Paper at innovative Applications Track, AAMAS 2015). Simpson further developed the Bayesian heatmaps component, detailed at ECML-PKDD 2017 and used to produce disaster maps for UN, FEMA and >60 NGOs after the Nepal 2015 and Ecuador 2016 earthquakes and Irma and Maria 2017 hurricanes. The heatmaps generated media attention, e.g., New Scientist (30/09/2015), Nature news (03/05/2016), Homeland Security Newswire (28/09/2017), publication at NeurIPS ML4D workshop (2018), EGU conference presentation (2019). Open source repository (pyIBCC) was the basis of Centre for Defence Enterprise grant CDE42106.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -