Monte Carlo Tree Search Algorithm: Understanding the Steps

Monte Carlo Tree Search Steps

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm widely used in artificial intelligence, especially in game-playing strategies. The algorithm involves four fundamental steps that are crucial in evaluating potential moves in a game:

1. Selection: The process begins from the root node, and the algorithm traverses the tree by selecting optimal child nodes until it reaches a leaf node.
2. Expansion: If the leaf node does not indicate the end of the game, one or more child nodes are added to expand the tree further.
3. Simulation: A simulation is then performed from the newly added nodes to generate an outcome based on the current state of the game.
4. Backpropagation: The result of the simulation is utilized to update the information stored in the nodes traversed from the root node to the leaf node.

Therefore, the correct sequence of steps in the Monte Carlo Tree Search algorithm is Selection, Expansion, Simulation, and Backpropagation. This structured approach aids in strategic decision-making and evaluation of potential moves in various applications, particularly in artificial intelligence systems designed for game playing.