Editing Computer chess (section)

==== Monte Carlo tree search ====
{{further|Monte Carlo tree search}}

Monte Carlo tree search (MCTS) is a heuristic search algorithm which expands the search tree based on random sampling of the search space. A version of Monte Carlo tree search commonly used in computer chess is PUCT, Predictor and Upper Confidence bounds applied to Trees.

DeepMind's [[AlphaZero]] and [[Leela Chess Zero]] uses MCTS instead of minimax. Such engines use [[batch processing|batching]] on [[graphics processing units]] in order to calculate their [[evaluation function]]s and policy (move selection), and therefore require a [[parallel computing|parallel]] search algorithm as calculations on the GPU are inherently parallel. The minimax and alpha-beta pruning algorithms used in computer chess are inherently serial algorithms, so would not work well with batching on the GPU. On the other hand, MCTS is a good alternative, because the random sampling used in Monte Carlo tree search lends itself well to parallel computing, and is why nearly all engines which support calculations on the GPU use MCTS instead of alpha-beta.