When did Monte Carlo tree search emerge as a concept?
The Monte Carlo method emerged in the 1940s as a way to solve deterministic problems using random sampling. Researchers began applying these ideas to game playing software by the late 1980s.
The Monte Carlo method emerged in the 1940s as a way to solve deterministic problems using random sampling. Researchers began applying these ideas to game playing software by the late 1980s.
Rémi Coulom coined the term Monte Carlo tree search in 2006 while applying the Monte Carlo method to game-tree search. That same year Levente Kocsis and Csaba Szepesvári developed the Upper Confidence bounds applied to Trees algorithm known as UCT.
In March 2016 AlphaGo defeated Lee Sedol four games to one earning an honorary 9-dan title. The system used Monte Carlo tree search together with artificial neural networks for both policy and value estimation.
Monte Carlo tree search offers advantages over alpha-beta pruning especially in games with high branching factors. It does not require an explicit evaluation function since implementing game mechanics alone suffices to explore the search space.
Google Deepmind released AlphaGo in October 2015 becoming the first computer program to beat a professional human Go player without handicaps on a standard 19x19 board.