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pro vyhledávání: '"Rebstock, Douglas"'
Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large. This challenge is particularly evident in trick-taking card games. While state sa
Externí odkaz:
http://arxiv.org/abs/2404.13150
Historically applied exclusively to perfect information games, depth-limited search with value functions has been key to recent advances in AI for imperfect information games. Most prominent approaches with strong theoretical guarantees require subga
Externí odkaz:
http://arxiv.org/abs/2311.14651
Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions. This makes the number of histories exponentially large in the action sequence length, as well as creating extremely la
Externí odkaz:
http://arxiv.org/abs/1905.10911
Decision-making in large imperfect information games is difficult. Thanks to recent success in Poker, Counterfactual Regret Minimization (CFR) methods have been at the forefront of research in these games. However, most of the success in large games
Externí odkaz:
http://arxiv.org/abs/1905.10907
Publikováno v:
Vol 33 (2019): Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Pages 1158-1165
In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values. While the evaluation component is vital, the accuracy of move value estimates is also fundamentally linked to how well the samp
Externí odkaz:
http://arxiv.org/abs/1903.09604
Autor:
Moore, Charles, Rebstock, Douglas, Katz, Ira M., Noga, Michelle L., Caillibotte, Georges, Finlay, Warren H., Martin, Andrew R.
Publikováno v:
In Journal of Biomechanics 9 November 2020 112
