Monte Carlo tree search
Heuristic search algorithm for evaluating game trees / From Wikipedia, the free encyclopedia
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In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree.
MCTS was combined with neural networks in 2016[1] and has been used in multiple board games like Chess, Shogi,[2] Checkers, Backgammon, Contract Bridge, Go, Scrabble, and Clobber[3] as well as in turn-based-strategy video games (such as Total War: Rome II's implementation in the high level campaign AI[4]).