# Knowledge-Based Paranoia Search in Trick-Taking

arXiv: https://arxiv.org/abs/2104.05423 · HTML: https://mlsanity.com/paper/2104.05423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 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)

This paper proposes knowledge-based paraonoia search (KBPS) to find forced wins during trick-taking in the card game Skat; for some one of the most interesting card games for three players. It combines efficient partial information game-tree search with knowledge representation and reasoning. This worst-case analysis, initiated after a small number of tricks, leads to a prioritized choice of cards. We provide variants of KBPS for the declarer and the opponents, and an approximation to find a forced win against most worlds in the belief space. Replaying thousands of expert games, our evaluation indicates that the AIs with the new algorithms perform better than humans in their play, achieving an average score of over 1,000 points in the agreed standard for evaluating Skat tournaments, the extended Seeger system.