All-Moves-As-First Heuristics in Monte-Carlo Go

We present and explore the effectiveness of sev- eral variations on the All-Moves-As-First (AMAF) heuristic in Monte-Carlo Go. Our results show that: Random play-outs provide more information about the goodness of moves made earlier in the play-out. AMAF updates are not just a way to quickly initialize counts, they are useful after every play-out. Updates even more aggressive than AMAF can be even more beneficial.