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.
