Strategy games are a challenge for the design of artificial intelligence agents due to their complexity and the combinatorial search space they produce. State abstraction has been applied in different domains to shrink the search space. Automatic state abstraction methods have gained much success in the planning domain and their transfer to strategy games raises a question of scalability. In this article, we propose elastic Monte Carlo tree search (MCTS), an algorithm that uses automatic state abstraction to play strategy games. In elastic MCTS, tree nodes are clustered dynamically. First, nodes are grouped by state abstraction for efficient exploration, to later be separated for refining exploitable action sequences. Such an elastic tree benefits from efficient information sharing while avoiding using an imperfect state abstraction during the whole search process. We provide empirical analyses of the proposed method in three strategy games of different complexity. Our empirical results show that in all games, elastic MCTS outperforms MCTS baselines by a large margin, with a considerable search tree size reduction at the expense of small computation time.

中文翻译：

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弹性蒙特卡罗树搜索

由于策略游戏的复杂性及其产生的组合搜索空间，策略游戏对人工智能代理的设计来说是一个挑战。状态抽象已应用于不同领域以缩小搜索空间。自动状态抽象方法在规划领域取得了很大成功，并将其转移到策略游戏中提出了可扩展性问题。在本文中，我们提出了弹性蒙特卡罗树搜索（MCTS），这是一种使用自动状态抽象来玩策略游戏的算法。在弹性MCTS中，树节点是动态聚类的。首先，节点按状态抽象进行分组，以进行有效的探索，然后进行分离以细化可利用的动作序列。这种弹性树受益于有效的信息共享，同时避免在整个搜索过程中使用不完美的状态抽象。我们在三种不同复杂度的策略游戏中对所提出的方法进行了实证分析。我们的实证结果表明，在所有游戏中，弹性 MCTS 的性能大幅优于 MCTS 基线，搜索树大小显着减小，但计算时间较短。