Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction
- Submitting institution
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University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 32156886
- Type
- D - Journal article
- DOI
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10.1109/TSMC.2019.2958846
- Title of journal
- IEEE Transactions on Systems, Man and Cybernetics: Systems
- Article number
- -
- First page
- 1
- Volume
- 0
- Issue
- -
- ISSN
- 2168-2216
- Open access status
- Technical exception
- Month of publication
- December
- Year of publication
- 2019
- URL
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- Supplementary information
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- Request cross-referral to
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- 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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3
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This recent work fills in the research gap between the evolutionary multiagent systems and opponent modelling. The research was in collaboration with A/P Yaqing Hou and led to a successful research grant from the National Natural Science Foundation of China (# 61906032; Value: RMB 240,000; Multiagent Transfer Reinforcement Learning upon the Sub-Modular Function Optimization). The technique of predicting opponent behavior also underpins the collaboration with Modus Seabed Intervention Ltd in an Innovate UK-KTP (#11646; Value: £113,554.00; To develop, trial and launch a capability for multiple autonomous vehicle platforms to operate and work collaboratively using Artificial Intelligence).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -