A Doppelkopf Player Based on UCT

We propose doppelkopf, a trick-taking card game with similarities to skat, as a benchmark problem for AI research. While skat has been extensively studied by the AI community in recent years, this is not true for doppelkopf. However, it has a substantially larger state space than skat and a unique key feature which distinguishes it from skat and other card games: players usually do not know with whom they play at the start of a game, figuring out the parties only in the process of playing.
Since its introduction in 2006, the UCT algorithm has been the dominating approach for solving games in AI research. It has notably achieved a playing strength comparable to good human players at playing go, but it has also shown good performance in card games like Klondike solitaire and skat. In this work, we adapt UCT to play doppelkopf and present an algorithm that generates random card assignments, used by the UCT algorithm for sampling. In our experiments, we discuss and evaluate different variants of the UCT algorithm, and we show that players based on UCT improve over simple baseline players and exhibit good card play behavior also when competing with a human player.