14 citations · 14 across the 3 of their papers we have counts for
5 papers
Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
Douglas Rebstock, Christopher Solinas, Nathan R. Sturtevant +1
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 challe…
History Filtering in Imperfect Information Games: Algorithms and Complexity
Christopher Solinas, Douglas Rebstock, Nathan R. Sturtevant +1
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…
Policy Based Inference in Trick-Taking Card Games
Douglas Rebstock, Christopher Solinas, Michael Buro +1
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…
Learning Policies from Human Data for Skat
Douglas Rebstock, Christopher Solinas, Michael Buro
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…
Improving Search with Supervised Learning in Trick-Based Card Games
Christopher Solinas, Douglas Rebstock, Michael Buro
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…