# On the Power of Refined Skat Selection

arXiv: https://arxiv.org/abs/2104.02997 · HTML: https://mlsanity.com/paper/2104.02997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [The αμ Search Algorithm for the Game of Bridge](https://mlsanity.com/paper/1911.07960/) (2019)
- [Improving Search with Supervised Learning in Trick-Based Card Games](https://mlsanity.com/paper/1903.09604/) (2019)
- [Bidding in Spades](https://mlsanity.com/paper/1912.11323/) (2019)
- [Learning Policies from Human Data for Skat](https://mlsanity.com/paper/1905.10907/) (2019)
- [Policy Based Inference in Trick-Taking Card Games](https://mlsanity.com/paper/1905.10911/) (2019)

## Abstract (arXiv)

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.