# Study and Improvement of Search Algorithms in Multi-Player Perfect-Information Games

arXiv: https://arxiv.org/abs/2604.17378 · HTML: https://mlsanity.com/paper/2604.17378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 19 Apr 2026.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning to Play Two-Player Perfect-Information Games without Knowledge](https://mlsanity.com/paper/2008.01188/) (2020)
- [Minimax Strikes Back](https://mlsanity.com/paper/2012.10700/) (2020)
- [Completeness of Unbounded Best-First Game Algorithms](https://mlsanity.com/paper/2109.09468/) (2021)
- [Study and improvement of search algorithms in two-players perfect information games](https://mlsanity.com/paper/2505.09639/) (2025)
- [On some improvements to Unbounded Minimax](https://mlsanity.com/paper/2505.04525/) (2025)

## Abstract (arXiv)

In this article, we generalize Unbounded Minimax, the state-of-the-art search algorithm for zero sums two-player games with perfect information to the framework of multiplayer games with perfect information. We experimentally show that this generalized algorithm also achieves better performance than the main multiplayer search algorithms.