This paper presents the first experimental evaluation of four previously untested modifications of Unbounded Best-First Minimax algorithm. This algorithm explores the game tree by iteratively expanding the most promising sequences of actions based on the current partial game tree. We first evaluate the use of transposition tables, which convert the game tree into a directed acyclic graph by merging duplicate states. Second, we compare the original algorithm by Korf & Chickering with the variant proposed by Cohen-Solal, which differs in its backpropagation strategy: instead of stopping when a stable value is encountered, it updates values up to the root. This change slightly improves performance when value ties or transposition tables are involved. Third, we assess replacing the exact terminal evaluation function with the learned heuristic function. While beneficial when exact evaluations are costly, this modification reduces performance in inexpensive settings. Finally, we examine the impact of the completion technique that prioritizes resolved winning states and avoids resolved losing states. This technique also improves performance. Overall, our findings highlight how targeted modifications can enhance the efficiency of Unbounded Best-First Minimax.

本文首次对四种以往未测试的无界优先极小化算法改进进行了实验评估，旨在解决游戏树搜索效率不高的问题。研究提出的关键方法包括引入变换表、改进回传策略、替换精确终端评估函数以及优先处理已解决的胜利状态。这些改进显著提高了无界优先极小化算法的性能。

对无界极小化算法的一些改进