  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.
