Hi Fuming, The idea of improving the quality of simulation is more earlier, than Mogo’s paper, in the Appendix A of Remi Coulom’s CG2006 paper “Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search”(http://remi.coulom.free.fr/CG2006/CG2006.pdf):
The choice of a more clever probability distribution can improve the quality of the Monte-Carlo estimation.... I am not sure if Remi was the first one proposing this concept in Computer Go field, but Mogo definitely was not. I was in Amstertam attending Computer Olympiad 2007, in the team of Chinese chess program “Deep Elephant”. I played with Crazy Stone, Mogo and was very surprised to see they beat me. Afterwards, Mogo’s paper is so easy to understand/implement for me that trigger me to work on Computer Go. Indeed, Mogo has huge contributions, especially in the popularization of MCTS. I don’t mean to weaken or deny it, but just want to point out Crazy Stone’s great contributions. In Erica, I use CrazyStone-like simulations successfully. Mogo-type simulation almost does not help Erica at all. If we want to numerate the strongest programs, we cannot forget Fuego(2010 UEC Cup winner) and MyGoFriend(Computer Olympiad 2010, 9x9 winner). For academic progress, we cannot forget Crazy Stone. For practical development usage, we cannot forget GnuGo and GoGui released by Fuego team. There were really too many contributors in the past. Happy New Years to all. Aja
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