Fetching the paper…
Reading the bibliography…
Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker.
Kuhn, H.W.: A simplified two-person poker. Contributions to the Theory of Games 1
1950
Earlier work this paper cites.
1967
Earlier work this paper cites.
Fudenberg, D., Tirole, J.: Game theory, 1991. Cambridge, Massachusetts 393
1991
Earlier work this paper cites.
Koller, D., Megiddo, N., Von Stengel, B.: Fast algorithms for finding randomized strategies in game trees. In: Proceedings of the Twenty-sixth Annual ACM Symposium on Theory of Computing, pp. 750–759 (1994)
1994
Earlier work this paper cites.
Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT press, Massachusetts (1994)
1994
Earlier work this paper cites.
Battigalli, P., Siniscalchi, M.: Rationalization and incomplete information. Advances in Theoretical Economics 3
2003
Earlier work this paper cites.
Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic game theory, 2007. Google Scholar Google Scholar Digital Library Digital Library
2007
Earlier work this paper cites.
Zinkevich, M., Johanson, M., Bowling, M., Piccione, C.: Regret minimization in games with incomplete information. In: Advances in Neural Information Processing Systems, pp. 1729–1736 (2008)
2008
Earlier work this paper cites.
Johanson, M., Waugh, K., Bowling, M., Zinkevich, M.: Accelerating best response calculation in large extensive games. In: IJCAI, vol. 11, pp. 258–265 (2011)
2011
Earlier work this paper cites.
Johanson, M., Bard, N., Lanctot, M., Gibson, R.G., Bowling, M.: Efficient nash equilibrium approximation through monte carlo counterfactual regret minimization. In: AAMAS, pp. 837–846 (2012). Citeseer
2012
Earlier work this paper cites.
Bard, N., Hawkin, J., Rubin, J., Zinkevich, M.: The annual computer poker competition. AI Magazine 34
2013
Earlier work this paper cites.
Burch, N., Johanson, M., Bowling, M.: Solving imperfect information games using decomposition. In: AAAI, pp. 602–608 (2014)
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
Tammelin, O., Burch, N., Johanson, M., Bowling, M.: Solving heads-up limit Texas hold’em. In: Twenty-fourth International Joint Conference on Artificial Intelligence (2015)
2015
Cited alongside, same era.
Moravcik, M., Schmid, M., Burch, N., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., Bowling, M.: Deepstack: Expert-level artificial intelligence in heads-up no-limit poker. Science 356
2017
Cited alongside, same era.
Brown, N., Sandholm, T.: Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Science 359
2018
Cited alongside, same era.
Brown, N., Sandholm, T.: Superhuman ai for multiplayer poker. Science (6456), 885–890 (2019)
Farina, G., Kroer, C., Sandholm, T.: Online convex optimization for sequential decision processes and extensive-form games. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1917–1925 (2019)
2019
Later among the works it cites.
Zhang, B., Sandholm, T.: Sparsified linear programming for zero-sum equilibrium finding. In: International Conference on Machine Learning, pp. 11256–11267 (2020). PMLR
2020
Later among the works it cites.
Zamir, S.: Bayesian Games: Games with Incomplete Information. Springer, ??? (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Kovařík, V., Schmid, M., Burch, N., Bowling, M., Lisỳ, V.: Rethinking formal models of partially observable multiagent decision making. Artificial Intelligence, 103645 (2021)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Kovařík, V., Seitz, D., Lisỳ, V., Rudolf, J., Sun, S., Ha, K.: Value functions for depth-limited solving in zero-sum imperfect-information games. arXiv e-prints, 1906 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Schmid, M., Burch, N., Lanctot, M., Moravcik, M., Kadlec, R., Bowling, M.: Variance reduction in Monte Carlo counterfactual regret minimization (VR-MCCFR) for extensive form games using baselines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 2157–2164 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Burch, N., Moravcik, M., Schmid, M.: Revisiting cfr+ and alternating updates. Journal of Artificial Intelligence Research 64
2019
Cited alongside, same era.
Brown, N., Sandholm, T.: Solving imperfect-information games via discounted regret minimization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1829–1836 (2019)
2019
Cited alongside, same era.
Brown, N., Lerer, A., Gross, S., Sandholm, T.: Deep counterfactual regret minimization. In: International Conference on Machine Learning, pp. 793–802 (2019). PMLR
2019
Cited alongside, same era.
2021
Closest in time.
Seitz, D., Milyukov, N., Lisý, V.: Learning to guess opponent’s information in large partially observable games. In: AAAI Workshop on Reinforcement Learning in Games (2021)
2021
Closest in time.
Wikipedia: Dudo. https://en.wikipedia.org/wiki/Dudo . accessed: 2021-11-11 (2021)
2021
Closest in time.
Wikipedia: Battleship (game). https://en.wikipedia.org/wiki/Battleship_(game) . accessed: 2021-11-11 (2021)
2021
Closest in time.
Wikipedia: Stratego. https://en.wikipedia.org/wiki/Stratego . accessed: 2021-11-11 (2021)
2021
Closest in time.
2021
Closest in time.
(FAIR)†, M.F.A.R.D.T., Bakhtin, A., Brown, N., Dinan, E., Farina, G., Flaherty, C., Fried, D., Goff, A., Gray, J., Hu, H., et al
2022
Closest in time.