What do you understand by Monte Carlo Tree Search? Explain. 

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm that is commonly used in decision-making processes in artificial intelligence and game theory. The algorithm was first introduced in 2006 as a way to improve the performance of game-playing programs, particularly in complex games such as Go and chess.

The basic idea behind MCTS is to build a search tree, where each node represents a possible state of the game. Starting from the current state of the game, the algorithm repeatedly selects the most promising node to explore, based on a set of rules that balance exploration and exploitation of the tree. The algorithm then simulates one or more plays of the game from the selected node, using a random strategy to choose moves for the opponents. The result of the simulation is backpropagated up the tree, updating the statistics _______ _______ _____ _____ ________.
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