Efficient crowdsourcing of unknown experts using bounded multi-armed bandits
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2185
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2014.04.005
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 89
- Volume
- 214
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
10.1016/j.artint.2014.04.005
- 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
-
3
- Research group(s)
-
-
- Citation count
- 56
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Develops the first use of multi-arm bandit algorithms in crowd sourcing applications and gives theoretical guarantees on performance, showing practical application on real-world datasets. The work was a key contribution of Long's PhD thesis (supervised by Jennings), which was a runner up in both the CPHC/BCS Distinguished Dissertation Award and the European AI Association's Distinguished Dissertation Award. The bandit algorithms formed the conceptual basis of a joint EPSRC-Singapore grant (EP/N02026X/1; £200K, £300K for NTU) for research on cyber security for smart traffic control systems (with Bo An from NTU).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
