Study and improvement of search algorithms in two-players perfect information games
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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.
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
4 of 26 references.
References · 4
- 2018Reinforcement Learning of Theorem Provingcited by 69
- 2019Ludii -- The Ludemic General Game Systemcited by 39
- 2020Monte Carlo Game Solvercited by 3
- 2019Comparative study of performance of parallel Alpha Beta Pruning for different architecturescited by 1
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025On some improvements to Unbounded Minimaxcited by 2
- 2026Minibal: Balanced Game-Playing Without Opponent Modelingcited by 0
- 2026Completeness of Unbounded Best-First Minimax and Descent Minimaxcited by 0
- 2026Study and Improvement of Search Algorithms in Multi-Player Perfect-Information Gamescited by 0
- 2026Study and Improvement of Search Algorithms in Multi-Player Perfect-Information Games19 Apr 2026
- 2026Completeness of Unbounded Best-First Minimax and Descent Minimax25 Mar 2026
- 2026Minibal: Balanced Game-Playing Without Opponent Modeling24 Mar 2026
- 2025On some improvements to Unbounded Minimax7 May 2025