Efficient implementation of breadth first search for general video game playing

Suguru Ito, Zikun Guo, C. Chu, Tomohiro Harada, R. Thawonmas
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Abstract

This paper proposes an efficient implementation of Breadth First Search (BFS) for General Video Game Playing (GVGP). This method is specialized for deterministic games, which often cannot be solved by a single action and require a more extensive search to solve. Most existing AI programs cannot search the game space efficiently, and thus perform poorly in deterministic games. To improve the efficiency of tree search, we propose limiting the branching of game tree in BFS. Hash code is assigned to each tree node and used to identify similar game states. A tree node with a game state that has been visited in previous search will not be expanded. Using a deterministic game set for evaluation, our experiment shows that the proposed method outperforms existing methods.
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有效地实现了一般视频游戏的广度优先搜索
提出了一种基于广度优先搜索(BFS)的通用视频游戏(GVGP)的高效实现方法。这种方法专门用于确定性游戏,这类游戏通常无法通过单一行动解决,需要更广泛的搜索才能解决。大多数现有的AI程序不能有效地搜索游戏空间,因此在确定性游戏中表现不佳。为了提高树搜索的效率,我们提出了在BFS中限制博弈树的分支。哈希码被分配到每个树节点,用于识别相似的游戏状态。在之前的搜索中访问过的具有游戏状态的树节点将不会被展开。使用确定性博弈集进行评估,我们的实验表明,所提出的方法优于现有方法。
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