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Search has played a fundamental role in computer game research since the very beginning.
Rethinking formal models of partially observable multiagent decision making
Kovařík, V.; Schmid, M.; Burch, N.; Bowling, M.; and Lisý, V. 2019 · 1906
Earlier work this paper cites.
Value Functions for Depth-Limited Solving in Imperfect-Information Games beyond Poker
Seitz, D.; Kovarík, V.; Lisỳ, V.; Rudolf, J.; Sun, S.; and Ha, K. 2019 · 1906
Earlier work this paper cites.
A simplified two-person poker
Kuhn, H. W. 1950 · 1950
Earlier work this paper cites.
XXII. Programming a computer for playing chess
Shannon, C. E. 1950 · 1950
Earlier work this paper cites.
Some studies in machine learning using the game of checkers
Samuel, A. L. 1959 · 1959
Earlier work this paper cites.
A course in game theory
Osborne, M. J.; and Rubinstein, A. 1994 · 1994
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Search in games with incomplete information: A case study using bridge card play
Frank, I.; and Basin, D. 1998 · 1998
Earlier work this paper cites.
Deep blue
Campbell, M.; Hoane Jr, A. J.; and Hsu, F.-h. 2002 · 2002
Cited alongside, same era.
The essential Turing
Copeland, B. J. 2004 · 2004
Cited alongside, same era.
Effective short-term opponent exploitation in simplified poker
Hoehn, B.; Southey, F.; Holte, R. C.; and Bulitko, V. 2005 · 2005
Cited alongside, same era.
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Brown, N.; Bakhtin, A.; Lerer, A.; and Gong, Q. 2020 · 2007
Cited alongside, same era.
Regret minimization in games with incomplete information
Zinkevich, M.; Johanson, M.; Bowling, M.; and Piccione, C. 2008 · 2008
Cited alongside, same era.
Monte Carlo sampling for regret minimization in extensive games
Online Monte Carlo counterfactual regret minimization for search in imperfect information games
Lisý, V.; Lanctot, M.; and Bowling, M. 2015 · 2015
Later among the works it cites.
Safe and nested subgame solving for imperfect-information games
Brown, N.; and Sandholm, T. 2017 · 2017
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Moravcik, M.; Schmid, M.; Burch, N.; Lisý, V.; Morrill, D.; Bard, N.; Davis, T.; Waugh, K.; Johanson, M.; and Bowling, M. 2017 · 2017
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Mastering the game of go without human knowledge
Silver, D.; Schrittwieser, J.; Simonyan, K.; Antonoglou, I.; Huang, A.; Guez, A.; Hubert, T.; Baker, L.; Lai, M.; Bolton, A.; et al. 2017 · 2017
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Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Brown, N.; and Sandholm, T. 2018 · 2018
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Superhuman AI for multiplayer poker
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Lanctot, M.; Waugh, K.; Zinkevich, M.; and Bowling, M. 2009 · 2009
Cited alongside, same era.
Solving imperfect information games using decomposition
Burch, N.; Johanson, M.; and Bowling, M. 2014 · 2014
Cited alongside, same era.
Brown, N.; and Sandholm, T. 2019 · 2019
Later among the works it cites.
Monte Carlo continual resolving for online strategy computation in imperfect information games
Šustr, M.; Kovařík, V.; and Lisý, V. 2019 · 2019
Later among the works it cites.