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Monte Carlo Tree Search (MCTS) is a decision-making algorithm commonly used in artificial intelligence (AI) for game playing, specifically in situations where the full search space is too large to explore exhaustively. It is often applied in domains such as board games like Go, chess, and other strategy-based games where the number of potential moves is vast. MCTS uses random sampling and probabilistic methods to explore the game tree incrementally, providing a balance between exploration (trying new, uncertain actions) and exploitation (choosing moves that have worked well in the past). The core idea behind ______ ______ ________ _____ ______ __________ _________ ______ ________.
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