Fetching the paper…
Reading the bibliography…
In zero-sum games, the optimal strategy is well-defined by the Nash equilibrium.
Safe strategies for agent modelling in games. In AAAI Fall Symposium on Artificial Multi-agent Learning . 103–110
Peter McCracken and Michael Bowling. 2004 · 2004
Earlier work this paper cites.
Computing robust counter-strategies. In Advances in neural information processing systems . 721–728
Michael Johanson, Martin Zinkevich, and Michael Bowling. 2008 · 2008
Earlier work this paper cites.
Regret minimization in games with incomplete information. In Advances in Neural Information Processing Systems . 1729–1736
Martin Zinkevich, Michael Johanson, Michael Bowling, and Carmelo Piccione. 2008 · 2008
Earlier work this paper cites.
Data biased robust counter strategies. In Artificial Intelligence and Statistics . 264–271
Michael Johanson and Michael Bowling. 2009 · 2009
Earlier work this paper cites.
Bayes’ bluff: Opponent modelling in poker
Finnegan Southey, Michael P Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, and Chris Rayner. 2012 · 2012
Earlier work this paper cites.
Online implicit agent modelling. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems . 255–262
Nolan Bard, Michael Johanson, Neil Burch, and Michael Bowling. 2013 · 2013
Earlier work this paper cites.
Kevin B Korb, Ann Nicholson, and Nathalie Jitnah. 2013 · 2013
Earlier work this paper cites.
Solving imperfect information games using decomposition. In Twenty-eighth AAAI conference on artificial intelligence
Neil Burch, Michael Johanson, and Michael Bowling. 2014 · 2014
Earlier work this paper cites.
Using response functions to measure strategy strength. In Twenty-Eighth AAAI Conference on Artificial Intelligence
Trevor Davis, Neil Burch, and Michael Bowling. 2014 · 2014
Earlier work this paper cites.
Safe opponent exploitation
Sam Ganzfried and Tuomas Sandholm. 2015 · 2015
Cited alongside, same era.
Opponent modeling by expectation–maximization and sequence prediction in simplified poker
Richard Mealing and Jonathan L Shapiro. 2015 · 2015
Cited alongside, same era.
Refining subgames in large imperfect information games. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 30
Matej Moravcik, Martin Schmid, Karel Ha, Milan Hladik, and Stephen Gaukrodger. 2016 · 2016
Cited alongside, same era.
Safe and nested endgame solving for imperfect-information games. In Workshops at the thirty-first AAAI conference on artificial intelligence
Noam Brown and Tuomas Sandholm. 2017 · 2017
Cited alongside, same era.
Targeted cfr. In Workshops at the thirty-first AAAI conference on artificial intelligence
Eric Griffin Jackson. 2017 · 2017
Cited alongside, same era.
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . 464–470
Edward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr TImbers, and Karl Tuyls. 2019 · 2019
Later among the works it cites.
Approximate exploitability: Learning a best response in large games
Finbarr Timbers, Edward Lockhart, Marc Lanctot, Martin Schmid, Julian Schrittwieser, Thomas Hubert, and Michael Bowling. 2020 · 2020
Later among the works it cites.
Unlocking the potential of deep counterfactual value networks
Ryan Zarick, Bryan Pellegrino, Noam Brown, and Caleb Banister. 2020 · 2020
Later among the works it cites.
Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 5575–5583
David Milec, Jakub Černý, Viliam Lisý, and Bo An. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Eqilibrium approximation quality of current no-limit poker bots. In Workshops at the Thirty-First AAAI Conference on Artificial Intelligence
Viliam Lisý and Michael Bowling. 2017 · 2017
Cited alongside, same era.
Deepstack: Expert-level artificial intelligence in Heads-Up No-Limit Poker
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling. 2017 · 2017
Cited alongside, same era.
Superhuman AI for multiplayer poker
Noam Brown and Tuomas Sandholm. 2019 · 2019
Cited alongside, same era.
Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, et al · 2021
Closest in time.
L2E: Learning to Exploit Your Opponent
Zhe Wu, Kai Li, Enmin Zhao, Hang Xu, Meng Zhang, Haobo Fu, Bo An, and Junliang Xing. 2021 · 2021
Closest in time.
Safe Opponent-Exploitation Subgame Refinement. In Advances in Neural Information Processing Systems
Mingyang Liu, Chengjie Wu, Qihan Liu, Yansen Jing, Jun Yang, Pingzhong Tang, and Chongjie Zhang. 2022 · 2022
Closest in time.
Efficient opponent exploitation in no-limit Texas hold’em poker: A neuroevolutionary method combined with reinforcement learning
Jiahui Xu, Jing Chen, and Shaofei Chen. 2021 · 2087
Closest in time.