Policy Based Inference in Trick-Taking Card Games
This work addresses the challenge of handling private information in complex card games for AI systems, representing an incremental improvement over prior methods.
The paper tackles the problem of large information sets in trick-taking card games by introducing a Policy Based Inference (PI) algorithm that uses player modeling to estimate state probabilities, showing it vastly improves inference and boosts the performance of the state-of-the-art Skat AI system Kermit.
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 large information sets. As a result, these games become too large to solve. To deal with these issues many algorithms employ inference, the estimation of the probability of states within an information set. In this paper, we demonstrate a Policy Based Inference (PI) algorithm that uses player modelling to infer the probability we are in a given state. We perform experiments in the German trick-taking card game Skat, in which we show that this method vastly improves the inference as compared to previous work, and increases the performance of the state-of-the-art Skat AI system Kermit when it is employed into its determinized search algorithm.