Monte Carlo Tree Search (MCTS)

Monte Carlo Tree Search (MCTS) is an algorithm used in the field of computer science and artificial intelligence for making optimal decisions in a given domain by performing random simulations of possible outcomes. It is particularly well-suited for games and problems with a large branching factor where traditional search strategies are impractical. MCTS combines the generality of random sampling with the precision of tree search.

The process involves four main steps: selection (choosing which node to explore next based on a strategy), expansion (adding one or more child nodes to the tree), simulation (playing out a random playout from the node), and backpropagation (updating the nodes with the results of the simulation). Over time, the algorithm builds a search tree and uses the accumulated results to make the most informed decision at each step.

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MCTS has been famously used in board game AI, such as in Google DeepMind's AlphaGo, which defeated world champion Go players. In Go, the vast number of possible moves makes exhaustive search impractical, so MCTS is used to efficiently explore the most promising moves through simulation. The algorithm evaluates potential moves by simulating random games (or playouts) from each move and using the results of these simulations to estimate the move's value.

Over many iterations, the algorithm converges on the most promising moves, guiding the AI's decision-making in the game. Another application is in robotics, where MCTS can be used for real-time planning and decision-making in uncertain environments, allowing robots to adapt their strategies based on the outcomes of simulated actions.

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