Monte Carlo Tree Search (MCTS) is a powerful search algorithm used in artificial intelligence, particularly for making decisions in complex, sequential games like chess, Go, and many modern video games. It combines elements of randomness (Monte Carlo simulations) with tree search to efficiently explore and evaluate possible moves or actions, helping AI systems make optimal choices even when the outcomes are uncertain or the decision space is vast. The approach is known for balancing exploration (trying new possibilities) and exploitation (choosing known successful options) in order to improve its decision-making over time.

MCTS works by building a “tree” of possible future states or moves from the current game position. It involves four main steps: selection, expansion, simulation, and backpropagation. In the selection phase, MCTS navigates down the existing tree to select a promising path based on criteria like prior success rates or the potential for reward. During expansion, it adds new potential moves to this tree, broadening the search. In the simulation phase, it performs random simulations from these new positions, effectively “imagining” what might happen in future moves. Finally, in backpropagation, it updates the scores of moves in the tree based on the results of these simulations, so the AI has better information for future choices.

One of the reasons MCTS is so widely used in AI gaming is its ability to handle vast numbers of possible outcomes with limited computational resources. Instead of evaluating every move like traditional brute-force methods, MCTS strategically explores only the most promising moves, making it feasible to tackle complex games with many branching options. This algorithm gained significant recognition when it was implemented in AlphaGo, Google’s AI program that defeated a human world champion in the game of Go.