  Skat is a fascinating combinatorial card game, show-casing many of the
intrinsic challenges for modern AI systems such as cooperative and adversarial
behaviors (among the players), randomness (in the deal), and partial knowledge
(due to hidden cards). Given the larger number of tricks and higher degree of
uncertainty, reinforcement learning is less effective compared to classical
board games like Chess and Go. As within the game of Bridge, in Skat we have a
bidding and trick-taking stage. Prior to the trick-taking and as part of the
bidding process, one phase in the game is to select two skat cards, whose
quality may influence subsequent playing performance drastically. This paper
looks into different skat selection strategies. Besides predicting the
probability of winning and other hand strength functions we propose hard
expert-rules and a scoring functions based on refined skat evaluation features.
Experiments emphasize the impact of the refined skat putting algorithm on the
playing performance of the bots, especially for AI bidding and AI game
selection.
