@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00106508,
 author = {Ting-FuLiao and I-ChenWu and Guan-WunChen and Chung-ChinShih and Po-YaKang and Bing-TsungChiang and Ting-ChuHo and Ti-RongWu and Ting-Fu, Liao and I-Chen, Wu and Guan-Wun, Chen and Chung-Chin, Shih and Po-Ya, Kang and Bing-Tsung, Chiang and Ting-Chu, Ho and Ti-Rong, Wu},
 book = {ゲームプログラミングワークショップ2014論文集},
 month = {Oct},
 note = {Monte-Carlo tree search (MCTS) has been successful on improving the strength of the game Go as well as many other game playing programs. For MCTS, one of the critical issues to further improve strength is parallelization. In order to deal with parallel MCTS generally, this paper designs a software framework for developing computer game programs with parallel MCTS. This framework hides the details of game-independent designs from computer game developers, so that developers can concentrate on improving heuristics related to game-specific knowledge. In this framework, we used lock-free tree parallelization inside a shared-memory system, and root parallelization over a distributed-memory system. For demonstration, we implemented a Go program named AMIGO, a Chinese dark chess program, and a puzzle program on top of this framework. The experimental results of AMIGO for 9x9 Go also show reasonable speedups and the strength improvement., Monte-Carlo tree search (MCTS) has been successful on improving the strength of the game Go as well as many other game playing programs. For MCTS, one of the critical issues to further improve strength is parallelization. In order to deal with parallel MCTS generally, this paper designs a software framework for developing computer game programs with parallel MCTS. This framework hides the details of game-independent designs from computer game developers, so that developers can concentrate on improving heuristics related to game-specific knowledge. In this framework, we used lock-free tree parallelization inside a shared-memory system, and root parallelization over a distributed-memory system. For demonstration, we implemented a Go program named AMIGO, a Chinese dark chess program, and a puzzle program on top of this framework. The experimental results of AMIGO for 9x9 Go also show reasonable speedups and the strength improvement.},
 pages = {122--126},
 publisher = {情報処理学会},
 title = {A Study of Software Framework for Parallel Monte Carlo Tree Search},
 volume = {2014},
 year = {2014}
}