Carcassonne博弈中树的MCTS置信上界的进化算法
On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne
摘要：
蒙特卡罗树搜索（MCTS）是一种抽样最佳优先搜索最优决策的方法。MCTS的受欢迎程度是基于其在具有挑战性的双人游戏围棋中的非凡成绩，围棋被认为比国际象棋难得多，直到最近才被认为是人工智能方法不可行的。MCTS的成功在很大程度上取决于如何构建树，选择过程在其中起着基础性的作用。一种已被证明是可靠的特定选择机制是基于树的上置信界限，通常称为UCT。UCT试图通过考虑存储在MCTS的统计树中的值来很好地平衡探索和利用。但是，要使其正常工作，需要对MCTS UCT进行一些调优。在这项工作中，我们使用进化算法（EA）的发展数学表达式的目标，以取代UCT数学表达式。我们比较我们提出的方法，称为演化策略在MCTS（ES-MCTS）对五个变种的MCTS UCT，三个变种的星极小极大家庭的算法，以及随机控制器的卡尔卡松游戏。我们还使用我们提出的EA为基础的控制器的一个变种，被称为ES部分集成在MCTS。我们展示了ES-MCTS控制器如何能够胜过所有这10个智能控制器，包括强大的MCTS UCT控制器。
英文摘要：
Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-player based game Go, a game considered much harder than Chess and that until very recently was considered infeasible for Artificial Intelligence methods. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees, commonly referred as UCT. The UCT attempts to nicely balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT mathematical expression. We compare our proposed approach, called Evolution Strategy in MCTS (ES-MCTS) against five variants of the MCTS UCT, three variants of the star-minimax family of algorithms as well as a random controller in the Game of Carcassonne. We also use a variant of our proposed EA-based controller, dubbed ES partially integrated in MCTS. We show how the ES-MCTS controller, is able to outperform all these 10 intelligent controllers, including robust MCTS UCT controllers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
