# Endgame Analysis of Dou Shou Qi

arXiv: https://arxiv.org/abs/1604.07312 · HTML: https://mlsanity.com/paper/1604.07312/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2016.

## Built on by (most cited first, 11 of 11)

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- [FAMOSE: A ReAct Approach to Automated Feature Discovery](https://mlsanity.com/paper/2602.17641/) (2026)
- [PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise](https://mlsanity.com/paper/2403.08216/) (2024)
- [Federated Automated Feature Engineering](https://mlsanity.com/paper/2412.04404/) (2024)
- [LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought](https://mlsanity.com/paper/2505.15657/) (2025)
- [BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification](https://mlsanity.com/paper/2605.06117/) (2026)
- [Predictability of Machine Learning Algorithms and Related Feature Extraction Techniques](https://mlsanity.com/paper/2305.00449/) (2023)

## Abstract (arXiv)

Dou Shou Qi is a game in which two players control a number of pieces, each of them aiming to move one of their pieces onto a given square. We implemented an engine for analyzing the game. Moreover, we created a series of endgame tablebases containing all configurations with up to four pieces. These tablebases are the first steps towards theoretically solving the game. Finally, we constructed decision trees based on the endgame tablebases. In this note we report on some interesting patterns.