Go is a traditional strategy-based board game originating from China circa 4th century BCE.  It involves two players placing  black and white playing pieces called stones on a 19×19 grid. The relatively new field of Computer Go is a field of Artificial Intelligence that focuses on creating creating computer programs to autonomously play Go.

Monte Carlo methods have been developed for these computer programs to more effectively choose the most appropriate next move. To do so, the program simulates literally thousands of games from the current state, in the end choosing the best move. This method requires little domain knowledge of Go (knowledge of how to play the game) and thus leads to strong strategy but poor tactics.

To remedy the problem a new search technique was developed, called Upper Confidence Bounds Applied to Trees (UCT) which is a natural extension of Monte-Carlo Go programs. UCT is essentially “player memory” in that it remembers states of previous games and the outcomes of those games based on the chosen moves. It then guides the program towards the randomly generated moves that would yield the most successful result based on past “experience”.

Now beating the computer at Go is harder than ever.

http://en.wikipedia.org/wiki/Computer_Go#Monte-Carlo_methods

http://senseis.xmp.net/?UCT