Completeness of Unbounded Best-First Game Algorithms
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Abstract (arXiv)
In this article, we prove the completeness of the following game search algorithms: unbounded best-first minimax with completion and descent with completion, i.e. we show that, with enough time, they find the best game strategy. We then generalize these two algorithms in the context of perfect information multiplayer games. We show that these generalizations are also complete: they find one of the equilibrium points.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 11 Sep 2021.
Builds on
3 of 20 references.
References · 3
- 2020Learning to Play Two-Player Perfect-Information Games without Knowledgecited by 11
- 2020Minimax Strikes Backcited by 11
- 2019Multiplayer AlphaZerocited by 6
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2025On some improvements to Unbounded Minimaxcited by 2
- 2023Learning to Play Stochastic Two-player Perfect-Information Games without Knowledgecited by 1
- 2026Minibal: Balanced Game-Playing Without Opponent Modelingcited 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
- 2026Minibal: Balanced Game-Playing Without Opponent Modeling24 Mar 2026
- 2025On some improvements to Unbounded Minimax7 May 2025
- 2023Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge8 Feb 2023