Parameter-free tree style pipeline in asynchronous parallel game-tree search

Abstract

Asynchronous parallel game-tree search methods are effective in improving the playing strength by using many computers connected through relatively slow networks. In game-position parallelization, the master program manages a game-tree and distributes positions in the tree to workers. Then, each worker asynchronously searches the best move and the corresponding evaluation for its assigned position. We present a new method for constructing an appropriate master tree that provides more important moves with more workers on their subtrees to improve the playing strength. Our contribution introduces two advantages: (1) being parameter free in that users do not need to tune parameters through trial and error, and (2) efficiency suitable even for short-time matches, such as one second per move. We implemented our method in chess with a top-level chess program Stockfish and evaluated the playing strength through self-plays.We confirm that the playing strength improves with up to sixty workers.