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Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years.
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Monte carlo sampling for regret minimization in extensive games
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Monte-carlo planning in large pomdps
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Information set Monte Carlo tree search
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The Resistance: Avalon, 2012
Don Eskridge · 2012
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Efficient nash equilibrium approximation through monte carlo counterfactual regret minimization
Michael Johanson, Nolan Bard, Marc Lanctot, Richard Gibson, and Michael Bowling · 2012
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Measuring the size of large no-limit poker games
Michael Johanson · 2013
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David G Rand and Martin A Nowak · 2013
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A parameterized family of equilibrium profiles for three-player kuhn poker
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Michael Tomasello · 2014
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Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Chris L Baker, Julian Jara-Ettinger, Rebecca Saxe, and Joshua B Tenenbaum · 2017
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Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Noam Brown and Tuomas Sandholm · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
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Maintaining cooperation in complex social dilemmas using deep reinforcement learning
Adam Lerer and Alexander Peysakhovich · 2017
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Monte Carlo tree search for games with hidden information and uncertainty
Daniel Whitehouse · 2014
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Heads-up limit hold’em poker is solved
Michael Bowling, Neil Burch, Michael Johanson, and Oskari Tammelin · 2015
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Emergent bluffing and inference with Monte Carlo tree search
Peter I Cowling, Daniel Whitehouse, and Edward J Powley · 2015
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Friend and Foe: When to Cooperate, when to Compete, and how to Succeed at Both
Adam Galinsky and Maurice Schweitzer · 2015
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The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter
Joseph Henrich · 2015
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Solving heads-up limit texas hold’em
Oskari Tammelin, Neil Burch, Michael Johanson, and Michael Bowling · 2015
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisỳ, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling · 2017
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A multi-agent reinforcement learning model of common-pool resource appropriation
Julien Perolat, Joel Z Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, and Thore Graepel · 2017
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Deep counterfactual regret minimization
Noam Brown, Adam Lerer, Sam Gross, and Tuomas Sandholm · 2018
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Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2018
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OpenAI Five
OpenAI · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Learning to share and hide intentions using information regularization
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, and David J Schwab · 2018
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A generalised method for empirical game theoretic analysis
Karl Tuyls, Julien Perolat, Marc Lanctot, Joel Z Leibo, and Thore Graepel · 2018
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Leveling the playing field-fairness in ai versus human game benchmarks
Rodrigo Canaan, Christoph Salge, Julian Togelius, and Andy Nealen · 2019
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Theory of minds: Understanding behavior in groups through inverse planning
Michael Shum, Max Kleiman-Weiner, Michael L Littman, and Joshua B Tenenbaum · 2019
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