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

Abstract

Abstract — We present and explore the effectiveness of several 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.