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 MCTS is to build a search tree in which each node represents a state of the game. The algorithm proceeds by simulating random playouts from the current state to estimate the potential outcome of different moves. Through repeated simulations, MCTS gradually refines its understanding of the best moves to make in a given situation. The process of MCTS is typically divided into four phases: Selection, Expansion, Simulation, and Backpropagation. 1. Selection: Starting at the root node (which represents the current state of the game), the algorithm selects child nodes iteratively, guided by a strategy