Emulating Human Play in a Leading Mobile Card Game
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 545
- Type
- D - Journal article
- DOI
-
10.1109/tg.2018.2835764
- Title of journal
- IEEE Transactions on Games
- Article number
- -
- First page
- 386
- Volume
- 11
- Issue
- 4
- ISSN
- 2475-1502
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2018
- 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
-
5
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- An IEEE Transactions journal follow up to our paper 'Memory-Bounded Monte Carlo Tree Search' which won the overall best paper award at the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), USA, 2016 (17 full papers, 18 posters accepted; around 100 submissions - [www.aaai.org/Library/AIIDE/aiide16contents.php]). The paper combines a cutting-edge decision tree search algorithm (ISMCTS) with Machine learning using a large data set from our leading games industry collaborator (AI Factory - CEO [jeff.rollason@aifactory.co.uk]) to automate the process of nuancing AI gameplay to feel more human-like in a chart-topping game with over 5 million downloads.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -