Monte Carlo Tree Search (MCTS) is a branch of stochastic modeling that utilizes decision trees for optimization, mostly applied to artificial intelligence (AI) game players. This project imagines a game in which an AI player searches for a stationary target within a 2-D lattice. We analyze its behavior with different target distributions and compare its efficiency to the Levy Flight Search, a model for animal foraging behavior. In addition to simulated data analysis we prove two theorems about the convergence of MCTS when computation constraints neglected.

中文翻译：

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蒙特卡罗树搜索在二维格子上搜索单个目标搜索游戏

蒙特卡洛树搜索（MCTS）是随机建模的一个分支，它利用决策树进行优化，主要应用于人工智能（AI）游戏玩家。该项目设想一个游戏，其中AI玩家在2-D格子内搜索固定目标。我们分析了不同目标分布下的行为，并将其效率与Levy Flight Search（一种动物觅食行为模型）进行了比较。除了模拟数据分析外，我们还证明了在忽略计算约束时有关MCTS收敛的两个定理。