| Win prediction in multiplayer esports: Live professional match prediction VJ Hodge, S Devlin, N Sephton, F Block, PI Cowling, A Drachen IEEE Transactions on Games 13 (4), 368-379, 2019 | 105 | 2019 |
| Narrative bytes: Data-driven content production in esports F Block, V Hodge, S Hobson, N Sephton, S Devlin, MF Ursu, A Drachen, ... Proceedings of the 2018 ACM international conference on interactive …, 2018 | 71 | 2018 |
| Investigating distribution of practice effects for the learning of foreign language verb morphology in the young learner classroom RE Kasprowicz, E Marsden, N Sephton The Modern Language Journal 103 (3), 580-606, 2019 | 65 | 2019 |
| Win prediction in esports: Mixed-rank match prediction in multi-player online battle arena games V Hodge, S Devlin, N Sephton, F Block, A Drachen, P Cowling arXiv preprint arXiv:1711.06498, 2017 | 39 | 2017 |
| Heuristic move pruning in Monte Carlo Tree Search for the strategic card game Lords of War N Sephton, PI Cowling, E Powley, NH Slaven 2014 IEEE conference on computational intelligence and games, 1-7, 2014 | 37 | 2014 |
| Combining gameplay data with monte carlo tree search to emulate human play S Devlin, A Anspoka, N Sephton, P Cowling, J Rollason Proceedings of the AAAI Conference on Artificial Intelligence and …, 2016 | 31 | 2016 |
| An experimental study of action selection mechanisms to create an entertaining opponent N Sephton, PI Cowling, NH Slaven 2015 IEEE Conference on Computational Intelligence and Games (CIG), 122-129, 2015 | 14 | 2015 |
| Parallelization of information set monte carlo tree search N Sephton, PI Cowling, E Powley, D Whitehouse, NH Slaven 2014 IEEE Congress on Evolutionary Computation (CEC), 2290-2297, 2014 | 14 | 2014 |
| How the business model of customizable card games influences player engagement VJ Hodge, N Sephton, S Devlin, PI Cowling, N Goumagias, J Shao, ... IEEE Transactions on Games 11 (4), 374-385, 2018 | 13 | 2018 |
| Difficulty-skill balance does not affect engagement and enjoyment: a pre-registered study using artificial intelligence-controlled difficulty J Cutting, S Deterding, S Demediuk, N Sephton Royal Society Open Science 10 (2), 220274, 2023 | 6 | 2023 |
| Using bugs and viruses to teach artificial intelligence PI Cowling, R Fennell, R Hogg, G King, P Rhodes, N Sephton Proceedings of The International Conference on Computer Games: Artificial …, 2004 | 5 | 2004 |
| Using association rule mining to predict opponent deck content in Android: Netrunner N Sephton, PI Cowling, S Devlin, VJ Hodge, NH Slaven 2016 IEEE Conference on Computational Intelligence and Games (CIG), 1-8, 2016 | 4 | 2016 |
| Difficulty-skill balance does not affect engagement and enjoyment: A pre-registered study using AI-controlled difficulty J Cutting, S Deterding, S Demediuk, N Sephton PsyArXiv, 2022 | 3 | 2022 |
| Applying Artificial Intelligence and Machine Learning Techniques to Create Varying Play Style in Artificial Game Opponents N Sephton University of York, 2016 | 2 | 2016 |
| DEBS Graveyard Surveyor (prototype) T Pillatt, N Sephton, J Gray | | 2019 |
| How the Business Model of Customisable Card Games Influences Player Engagement N Goumagias, J Shao, K Purvis, I Cabras, KJ Fernandes, F Li | | 2018 |
| SPECIAL ISSUE ON TEAM AI IN GAMES VJ Hodge, S Devlin, N Sephton, F Block, PI Cowling, A Drachen, ... | | |
Peter I. CowlingProfessor of AI, Queen Mary University of LondonVerified email at qmul.ac.uk
