发表机构
Wrocław Centre for Networking and Supercomputing; Maastricht University（弗罗茨瓦夫网络与超级计算中心; 马斯特里赫特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对集成确定化蒙特卡洛树搜索，提出动态资源分配的增强措施，包括动态确定化数量和动态模拟分配，以三款桌面游戏为基准测试，特定配置能显著提升算法强度。
AI 中文摘要
基于模拟的算法特别适用于高不确定性环境，如具有大量随机性和隐藏信息的对抗性棋盘游戏。特别是，几种蒙特卡洛树搜索（MCTS）变体常用于此类领域。本文中，我们为集成确定化MCTS提出了一系列增强措施，引入了两个动态资源分配轴。首先是动态确定化数量，根据迄今为止的搜索行为增加或减少当前使用的确定化树的数量。其次是动态模拟分配，在确定化树之间非均匀地分配模拟预算，利用模拟到模拟的决策选择可能具有最佳知识增益的树。我们以三款流行桌面游戏：斋浦尔、失落之城和辉煌作为基准领域。在基于迭代和时间的设置中测试我们提出的增强措施表明，特定配置会使算法强度在统计上显著提高。
英文摘要
Simulation-based algorithms are especially suited for high-uncertainty environments such as adversarial board games with significant elements of randomness and hidden information. In particular, several Monte Carlo Tree Search (MCTS) variants are commonly used in such domains. In this paper, we propose a series of enhancements for Ensemble Determinization MCTS, introducing two axes for dynamic resource allocation. First, Dynamic Number of Determinizations, increases or decreases the number of currently used determinization trees depending on the behavior of so-far search. Second, Dynamic Simulation Allocation, splits the simulation budget nonuniformly across the determinization trees, using simulation-to-simulation decisions to choose the tree with potentially the best knowledge gain. As benchmark domains, we used three popular tabletop games: Jaipur, Lost Cities, and Splendor. Testing our proposed enhancements in iteration- and time-based settings showed that particular configurations yield a statistically significant increase in the algorithm's strength.
