Completeness of Unbounded Best-First Minimax and Descent Minimax
In this article, we focus on search algorithms for two-player perfect information games, whose objective is to determine the best possible strategy, and ideally a winning strategy. Unfortunately, some search algorithms for games in the literature are not able to always determine a winning strategy, even with an infinite search time. This is the case, for example, of the following algorithms: Unbounded Best-First Minimax and Descent Minimax, which are core algorithms in state-of-the-art knowledge-free reinforcement learning. They were then improved with the so-called completion technique. However, whether this technique sufficiently improves these algorithms to allow them to always determi...
| Authors | Quentin Cohen-Solal |
|---|---|
| Primary category | cs.AI |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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