MCTS stands for Monte Carlo Tree Search, which is a decision-making algorithm used in artificial intelligence and computer science. It is a heuristic search algorithm that is often employed in games and other domains with high levels of uncertainty.
In MCTS, a large search tree is used to represent possible moves and outcomes in a given game or problem space. The algorithm simulates a number of random game plays or simulations, known as Monte Carlo simulations, to build statistics about the likelihood of winning or losing from different positions.
The algorithm starts with a single root node representing the current state of the game or problem. It then repeatedly selects child nodes from the current position based on a selection policy, usually balancing a trade-off between exploration and exploitation. These selected nodes are expanded to explore possible moves or actions.
Once a certain resource limit, such as time or computation power, is reached, the simulations are run, usually in a playout phase, from the expanded nodes to produce an outcome. The statistics gathered during these simulations are then used to update the values associated with the nodes in the search tree.
MCTS is known for its ability to handle games or problems with large branching factors and complex decision spaces. It has been successfully applied in various domains, including board games like Go and Chess, video games, robot motion planning, and optimization problems. Its robustness and ability to adapt to different problem spaces make it a popular algorithm in the field of artificial intelligence and game theory.
