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:
Abstract. We present a new exploration term, more efficient than classical UCT-like exploration terms. It combines efficiently expert rules, patterns extracted from datasets, All-M...
We experimented a simple yet powerful optimization for Monte-Carlo Go tree search. It consists in dealing appropriately with strings that have two liberties. The heuristic is cont...
We describe two Go programs, ¢¡¤£¦¥ and ¢¡¤§¨£ , developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach. Our method is based on Abra...