Deepstack: Expert-level artificial intelligence in heads-up no-limit poker M Moravčík, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ... Science 356 (6337), 508-513, 2017 | 913 | 2017 |
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 | 243 | 2020 |
Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization. M Johanson, N Bard, M Lanctot, RG Gibson, M Bowling Aamas, 837-846, 2012 | 92 | 2012 |
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 | 85 | 2012 |
Online implicit agent modelling N Bard, M Johanson, N Burch, M Bowling Proceedings of the 2013 international conference on Autonomous agents and …, 2013 | 67 | 2013 |
Particle filtering for dynamic agent modelling in simplified poker N Bard, M Bowling Proceedings of the National Conference on Artificial Intelligence 22 (1), 515, 2007 | 37 | 2007 |
Optimal unbiased estimators for evaluating agent performance M Zinkevich, M Bowling, N Bard, M Kan, D Billings Proceedings of the National Conference on Artificial Intelligence 21 (1), 573, 2006 | 35 | 2006 |
Do pokers players know how good they are? Accuracy of poker skill estimation in online and offline players TL MacKay, N Bard, M Bowling, DC Hodgins Computers in Human Behavior 31, 419-424, 2014 | 27 | 2014 |
A demonstration of the Polaris poker system. MH Bowling, N Abou Risk, N Bard, D Billings, N Burch, J Davidson, ... AAMAS (2), 1391-1392, 2009 | 27 | 2009 |
Strategy grafting in extensive games K Waugh, N Bard, M Bowling Advances in Neural Information Processing Systems 22, 2009 | 25 | 2009 |
Player of games M Schmid, M Moravcik, N Burch, R Kadlec, J Davidson, K Waugh, N Bard, ... arXiv preprint arXiv:2112.03178, 2021 | 22 | 2021 |
Approximate exploitability: Learning a best response in large games F Timbers, N Bard, E Lockhart, M Lanctot, M Schmid, N Burch, ... arXiv preprint arXiv:2004.09677, 2020 | 14 | 2020 |
The annual computer poker competition N Bard, J Hawkin, J Rubin, M Zinkevich AI Magazine 34 (2), 112-112, 2013 | 13 | 2013 |
Decision-theoretic clustering of strategies N Bard, D Nicholas, C Szepesvaári, M Bowling Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015 | 12 | 2015 |
Asymmetric abstractions for adversarial settings N Bard, M Johanson, M Bowling Proceedings of the 2014 international conference on Autonomous agents and …, 2014 | 12 | 2014 |
The Trellis Security Infrastructure: A Layered Approach to Overlay Metacomputers. M Kan, D Ngo, M Lee, P Lu, N Bard, M Closson, M Ding, M Goldenberg, ... HPCS, 109-117, 2004 | 11 | 2004 |
Online agent modelling in human-scale problems NDC Bard | 7 | 2016 |
Human-agent cooperation in bridge bidding E Lockhart, N Burch, N Bard, S Borgeaud, T Eccles, L Smaira, R Smith arXiv preprint arXiv:2011.14124, 2020 | 5 | 2020 |
Deepstack: expert-level artificial intelligence in no-limit poker. CoRR abs/1701.01724 (2017) M Moravcık, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ... | 3 | |
Approximate Exploitability: Learning a Best Response F Timbers, N Bard, E Lockhart, M Lanctot, M Schmid, N Burch, ... Proceedings of the International Joint Conference on Artificial Intelligence …, 2022 | 2 | 2022 |