论文标题
蒙特卡洛树在二维晶格上寻找单个目标搜索游戏
Monte Carlo Tree Search for a single target search game on a 2-D lattice
论文作者
论文摘要
蒙特卡洛树搜索（MCT）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。该项目想象一个游戏，其中AI播放器在二维晶格中搜索固定目标。我们通过不同的目标分布分析其行为，并将其效率与征税飞行搜索进行比较，这是动物觅食行为的模型。除了模拟数据分析外，我们还证明了两个定理有关在忽略计算约束时MCT的收敛性的定理。
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.
