MCTS is a random algorithm, and AlphaGo is no exception. The AI selects a move. What state is the board in now? It doesn't know, because the opponent also sele

MCTS is a random algorithm, and AlphaGo is no exception. The AI selects a move. What state is the board in now? It doesn't know, because the opponent also selected a move. MCTS models this with a probability distribution of the states, and samples from this distribution repeatedly to build an estimate of the effectiveness of each move it could make. But what's the probability of each move made by the opponent? And after the simulation has looked as many moves ahead as it can in the time constraints, how good a position is it in? These are the same question, really - what's the chance of winning from this board state. In Chess you can use a heuristic algorithm to figure it out. In Go, you can't. But you can use a neural network to learn an approximation that improves as it sees more games complete. AlphaGo does this. MCTS is a random sampling technique, and the neural net informs its probability distributions, but doesn't make it deterministic.