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

Monte Carlo Tree Search (MCTS) is an advanced algorithmic technique used primarily for decision-making in artificial intelligence (AI), particularly in game-playing scenarios. It is a search algorithm that explores possible future game states by simulating random game plays and uses the results to make optimal decisions. The key feature of MCTS is its ability to balance exploration (trying new and untested moves) and exploitation (making moves based on what has already been found to be successful).

MCTS is particularly effective in environments with large decision spaces, such as board games like Go, Chess, or more complex simulations. The algorithm works by incrementally building a search tree, where each node represents a state of the game, and the edges between nodes represent possible moves or transitions between those states. The process of building and expanding the tree is done by repeatedly applying four _______ ____ ____ __________ ____ ____ ________ ________ __________ _____.
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