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It combines the random sampling of Monte Carlo simulation with the systematic nature of decision tree search. MCTS is very useful in the fields of computer game theory and artificial intelligence, especially in Go, chess, and other strategy games. . Basic steps of MCTS ) Selection: Starting from the root node, select the most promising child nodes according to a specific strategy until you reach a node that is not yet fully expanded (that is, there are still unexplored actions). ) Expansion: Add one or more child nodes to the selected node, which represent possible next actions.



This involves updating the game state, moving brazil email list the game forward to a new state. ) Simulation: Starting from the newly added node, a Monte Carlo simulation is performed until the game ends or a predetermined simulation depth is reached. This process does not require perfect information and can use stochastic strategies to select actions. ) Backward expansion: Update the simulation results (such as win or loss or scoring) to all nodes on the visited path. If the simulation result is a win, increase the number of wins at the nodes along the path, if a failure, update the failure statistics accordingly.



. Key Features ) Adaptive Search: MCTS can adaptively search for promising areas based on previous search results. ) Heuristic-free: Unlike some other search algorithms, MCTS does not require domain-specific heuristic evaluation functions. ) Parallelization: The simulation steps can be run independently, so MCTS is easily parallelized, making it particularly efficient on multi-core processors. . Comparison of Beam Search, Lookahead Search, and MCTS Beam Search: A heuristic graph search algorithm commonly used in the decoding process in machine translation, speech recognition, and other fields. It expands a certain number (beam width) of the most promising child nodes from the current node at each step instead of searching all possible child nodes, thus reducing the search space.