Opponent Modelling by Expectation-Maximisation and Sequence Prediction in Simplified Poker
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 40102832
- Type
- D - Journal article
- DOI
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10.1109/TCIAIG.2015.2491611
- Title of journal
- IEEE Transactions on Computational Intelligence and AI in Games
- Article number
- -
- First page
- 11
- Volume
- 9
- Issue
- 1
- ISSN
- 1943-0698
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- 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
-
1
- Research group(s)
-
A - Computer Science
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper contributes to this space by combining the state-of-the-art method for learning in hidden information games, with a long-established machine learning with hidden information to infer the hidden information possessed by opponents.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
