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Monte Carlo Tree Search (MCTS) is an advanced search algorithm widely used in decision-making processes, particularly in domains that involve large state spaces and strategic planning such as games, robotics, and artificial intelligence systems. It belongs to the family of heuristic search methods that aim to find the best possible decision by simulating possible future states and evaluating them through random sampling. MCTS does not require a complete evaluation of all possible moves, which makes it suitable for problems where the search space is extremely large and cannot be exhaustively explored. MCTS combines two important concepts: the construction of a search tree and the use of random simulations, or rollouts, to estimate the value of actions. It has been instrumental in achieving breakthroughs in artificial intelligence, notably in computer programs like AlphaGo, which defeated professional human players in the game of Go, a domain considered computationally intractable by traditional search methods. The core idea of MCTS is to iteratively build a tree that represents the possible states of a system or game, starting from an initial root node. Each node corresponds to a state, and each edge represents an action leading to another state. Unlike exhaustive search algorithms such as minimax, which evaluate all possible states, MCTS incrementally expands only the most promising parts of the tree __________ ________ ____ _____ ________ ______ _________ ________ ________ ____ _________.
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