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

Monte Carlo Tree Search (MCTS) is a popular algorithm used for decision-making in games and planning problems, particularly in artificial intelligence (AI) applications. MCTS is a search algorithm that employs randomness and simulations to make decisions by progressively building a search tree. It has been widely adopted in AI, especially for problems with large state spaces, where traditional search algorithms are computationally expensive or infeasible.

The core idea behind MCTS is to simulate a series of possible future actions and use the results of these simulations to guide the selection of the most promising moves. The algorithm consists of four main steps: Selection, Expansion, Simulation, and Backpropagation. These steps are repeated iteratively, gradually improving the quality of the decision-making process.

### Selection

The first step is selection, where the algorithm traverses the existing search tree, starting from the root node (which represents the current state of the game or problem). At each node, the algorithm chooses __________ ______ ____ ______ _________ _________ _____ _________ ___ ______ _________ __________.
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