Improving Search with Supervised Learning in Trick-Based Card Games
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 sampling distribution corresponds the true distribution. Despite this, recent work in trick-taking card game AI has mainly focused on improving evaluation algorithms with limited work on improving sampling. In this paper, we focus on the effect of sampling on the strength of a player and propose a novel method of sampling more realistic states given move history. In particular, we use predictions about locations of individual cards made by a deep neural network --- trained on data from human gameplay - in order to sample likely worlds for evaluation. This technique, used in conjunction with Perfect Information Monte Carlo (PIMC) search, provides a substantial increase in cardplay strength in the popular trick-taking card game of Skat.
Code (0)
등록된 구현이 없습니다.
Tasks
Card GamesSimilar Papers 제목 키워드 기반
Knowledge-Based Paranoia Search in Trick-Taking
This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find forced wins during trick-taking in the card game Skat; for some one of the most interesting card games for three players. It combines efficient p…
Card GamesPolicy Based Inference in Trick-Taking Card Games
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…
Card GamesTransformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
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-tak…
Card GamesHistory Filtering in Imperfect Information Games: Algorithms and Complexity
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 theore…
Card GamesDecision MakingSequential Decision MakingOuter-Learning Framework for Playing Multi-Player Trick-Taking Card Games: A Case Study in Skat
In multi-player card games such as Skat or Bridge, the early stages of the game, such as bidding, game selection, and initial card selection, are often more critical to the success of the play than refined middle- and en…