| DeepStack: Expert-level artificial intelligence in heads-up no- limit poker M Moravcik, M Schmid, N Burch, V Lisy, D Morrill, N Bard, T Davis, ... Science, 2017 | 1218 | 2017 |
| Checkers is solved J Schaeffer, N Burch, Y Bjornsson, A Kishimoto, M Muller, R Lake, P Lu, ... science 317 (5844), 1518-1522, 2007 | 673 | 2007 |
| Heads-up limit hold’em poker is solved M Bowling, N Burch, M Johanson, O Tammelin Science 347 (6218), 145-149, 2015 | 603 | 2015 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 424 | 2020 |
| Approximating game-theoretic optimal strategies for full-scale poker D Billings, N Burch, A Davidson, R Holte, J Schaeffer, T Schauenberg, ... IJCAI, 661-668, 2003 | 365 | 2003 |
| Bayes' bluff: Opponent modelling in poker F Southey, MP Bowling, B Larson, C Piccione, N Burch, D Billings, ... Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI-05), 2005 | 332* | 2005 |
| Solving Heads-up Limit Texas Hold’em O Tammelin, N Burch, M Johanson, M Bowling Proceedings of the 24th International Joint Conference on Artificial …, 2015 | 198 | 2015 |
| Mastering the game of Stratego with model-free multiagent reinforcement learning J Perolat, B De Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ... Science 378 (6623), 990-996, 2022 | 197 | 2022 |
| Bayesian action decoder for deep multi-agent reinforcement learning JN Foerster, F Song, E Hughes, N Burch, I Dunning, S Whiteson, ... Proceedings of the Thirty-Sixth International Conference on Machine Learning, 2019 | 180 | 2019 |
| Game-tree search with adaptation in stochastic imperfect-information games D Billings, A Davidson, T Schauenberg, N Burch, M Bowling, R Holte, ... Computers and Games, 21-34, 2004 | 121 | 2004 |
| Solving Imperfect Information Games Using Decomposition N Burch, M Johanson, M Bowling Proceedings of the Twenty-Eighth AAAI conference on Artificial Intelligence, 2014 | 119 | 2014 |
| Memory-based heuristics for explicit state spaces NR Sturtevant, A Felner, M Barrer, J Schaeffer, N Burch Twenty-First International Joint Conference on Artificial Intelligence, 2009 | 115 | 2009 |
| Finding optimal abstract strategies in extensive-form games M Johanson, N Bard, N Burch, M Bowling Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1371-1379, 2012 | 104 | 2012 |
| Evaluating state-space abstractions in extensive-form games M Johanson, N Burch, R Valenzano, M Bowling Proceedings of the 2013 international conference on Autonomous agents and …, 2013 | 100 | 2013 |
| Solving checkers J Schaeffer, Y Björnsson, N Burch, A Kishimoto, M M¨ uller, R Lake, P Lu, ... Proceedings of the 19th international joint conference on Artificial …, 2005 | 92 | 2005 |
| No-regret learning in extensive-form games with imperfect recall M Lanctot, R Gibson, N Burch, M Zinkevich, M Bowling arXiv preprint arXiv:1205.0622, 2012 | 91 | 2012 |
| Block A*: Database-driven search with applications in any-angle path-planning P Yap, N Burch, R Holte, J Schaeffer Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 120-125, 2011 | 89 | 2011 |
| From Poincaré recurrence to convergence in imperfect information games: Finding equilibrium via regularization J Perolat, R Munos, JB Lespiau, S Omidshafiei, M Rowland, P Ortega, ... International Conference on Machine Learning, 8525-8535, 2021 | 85 | 2021 |
| Online implicit agent modelling N Bard, M Johanson, N Burch, M Bowling Proceedings of the 2013 international conference on Autonomous agents and …, 2013 | 85 | 2013 |
| Variance reduction in monte carlo counterfactual regret minimization (VR-MCCFR) for extensive form games using baselines M Schmid, N Burch, M Lanctot, M Moravcik, R Kadlec, M Bowling Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence …, 2019 | 82 | 2019 |
