Participants : Olivier Teytaud [correspondent] , Hassen Doghmen, Jean-Baptiste Hoock.
MoGo and its Franco-Taiwanese counterpart MoGoTW is a Monte-Carlo Tree Search program for the game of Go, which made several milestones of computer-Go in the past (first wins against professional players in 19x19; first win with disadvantageous side in 9x9 Go); MoGo has had new developments as follows:
A Meta-MCTS module (inspired by the collaboration with Tristan Cazenave in the ANR EXPLO-RA project), which provided both a huge opening book in 9x9 and an approximate solving of 7x7 Go .
Following the “poolRave” modification, introduction of machine learning and statistics into MCTS, such as:
Bernstein Races  (for offline educating Monte-Carlo simulations).
These developments have been summarized and compared in  .
Variants of Go: we tested variants of Go, in particular blind variants; this suggests that in such frameworks playing theoretically suboptimal moves helps a lot, because such unnatural moves are harder to memorize. A preliminary related publication is  ; some additional results are to be published. Another interesting variant is random-Go, starting from a randomly generated board; such situations are much harder for humans, and, interestingly, our program was competitive in front of a 6D player (ranked 4th in a world amateur championship and former French champion) on a 19x19 board .
MoGo's development team was awarded the 2010 ChessBase award for the best contribution to Computer-Games.