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

arXiv 2604.17378 ↗First posted 19 Apr 2026cs.GT cs.AIcited by 0

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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.

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.

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References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Learning to Play Two-Player Perfect-Information Games without Knowledgecited by 11
  3. 2020Minimax Strikes Backcited by 11
  4. 2021Completeness of Unbounded Best-First Game Algorithmscited by 4
  5. 2025Study and improvement of search algorithms in two-players perfect information gamescited by 4
  6. 2025On some improvements to Unbounded Minimaxcited by 2

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