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Monte Carlo Tree Search (MCTS) is a sampling best-first method to search foroptimal decisions. The MCTS's popularity is based on its extraordinary resultsin the challenging two-player based game Go, a game considered much harder thanChess and that until very recently was considered infeasible for ArtificialIntelligence methods. The success of MCTS depends heavily on how the tree isbuilt and the selection process plays a fundamental role in this. Oneparticular selection mechanism that has proved to be reliable is based on theUpper Confidence Bounds for Trees, commonly referred as UCT. The UCT attemptsto nicely balance exploration and exploitation by considering the values storedin the statistical tree of the MCTS. However, some tuning of the MCTS UCT isnecessary for this to work well. In this work, we use Evolutionary Algorithms(EAs) to evolve mathematical expressions with the goal to substitute the UCTmathematical expression. We compare our proposed approach, called EvolutionStrategy in MCTS (ES-MCTS) against five variants of the MCTS UCT, threevariants of the star-minimax family of algorithms as well as a randomcontroller in the Game of Carcassonne. We also use a variant of our proposedEA-based controller, dubbed ES partially integrated in MCTS. We show how theES-MCTS controller, is able to outperform all these 10 intelligent controllers,including robust MCTS UCT controllers.

On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne