Monte Carlo Tree Search (MCTS)
Monte Carlo Tree Search (MCTS) is a heuristic search algorithm that is used in artificial intelligence (AI) to solve decision-making problems. It is a probabilistic algorithm that combines elements of both tree search and Monte Carlo simulation. MCTS works by iteratively exploring a game tree. The tree is a representation of the possible states of the game and the possible moves that can be made from each state. MCTS starts at the root of the tree, which represents the current state of the game. It then selects a child node of the root node, simulates a game from that node to a terminal state, and updates the values of the nodes in the tree based on the outcome of the simulation. This process is repeated until a leaf node is reached, or until a maximum number of iterations is reached. The values of the nodes in the tree are used to estimate the probability of winning from each state. The node with the highest probability is then selected as the best move. MCTS is a powerful algorithm ...