Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search.
One tremendous advantage of MCTS is that it lends itself to intelligent brute force decision making, and that the same engine can be applied to multiple games. There is no need for any heuristic to evaluate positions or to get the temperature of the current system; you simply play through until the end and then backpropogate your results, over and over and over again.
MCTS was introduced in 2006 as an algorithm for playing the game of Go, which had been resistant to attack by other methods.