20 citations · 21 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
Combining Strategic Learning and Tactical Search in Real-Time Strategy Games
Nicolas A. Barriga, Marius Stanescu, Michael Buro
A commonly used technique for managing AI complexity in real-time strategy (RTS) games is to use action and/or state abstractions. High-level abstractions can often lead to good st…