# Study and improvement of search algorithms in two-players perfect information games

arXiv: https://arxiv.org/abs/2505.09639 · HTML: https://mlsanity.com/paper/2505.09639/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 4 of 4)

- [Reinforcement Learning of Theorem Proving](https://mlsanity.com/paper/1805.07563/) (2018)
- [Ludii -- The Ludemic General Game System](https://mlsanity.com/paper/1905.05013/) (2019)
- [Monte Carlo Game Solver](https://mlsanity.com/paper/2001.05087/) (2020)
- [Comparative study of performance of parallel Alpha Beta Pruning for different architectures](https://mlsanity.com/paper/1908.11660/) (2019)

## Built on by (most cited first, 4 of 4)

- [On some improvements to Unbounded Minimax](https://mlsanity.com/paper/2505.04525/) (2025)
- [Minibal: Balanced Game-Playing Without Opponent Modeling](https://mlsanity.com/paper/2603.23059/) (2026)
- [Completeness of Unbounded Best-First Minimax and Descent Minimax](https://mlsanity.com/paper/2603.24572/) (2026)
- [Study and Improvement of Search Algorithms in Multi-Player Perfect-Information Games](https://mlsanity.com/paper/2604.17378/) (2026)

## Abstract (arXiv)

Games, in their mathematical sense, are everywhere (game industries, economics, defense, education, chemistry, biology, ...).Search algorithms in games are artificial intelligence methods for playing such games. Unfortunately, there is no study on these algorithms that evaluates the generality of their performance. We propose to address this gap in the case of two-player zero-sum games with perfect information. Furthermore, we propose a new search algorithm and we show that, for a short search time, it outperforms all studied algorithms on all games in this large experiment and that, for a medium search time, it outperforms all studied algorithms on 17 of the 22 studied games.