@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00071329,
 author = {金子, 知適 and 田中, 哲朗 and TOMOYUKI, KANEKO and TETSURO, TANAKA},
 book = {ゲームプログラミングワークショップ2010論文集},
 issue = {12},
 month = {Nov},
 note = {メモリを共有しないクラスタ環境においてゲーム木探索を並列に行う手法は古くから研究されているが,従来の手法はいずれもプログラムの大幅な改変を必要とした.本研究では,元のプログラムの変更をほとんど要しないような単純さを保ちながらも,全体の強さを向上させる分散並列探索の枠組みを提案する.提案する枠組みでは,各節点における最善手を予想しながら再帰的にマスターのゲーム木を成長させ,各葉にスレーブが一つづつ割り当てられるまでゲーム木を成長させた後に残りの探索をスレーブに任せる.GPS将棋を用いた実験からは上位2手に集中的に資源を割り当てる単純な仕組みでも効果的に機能し,8スレーブの分散探索では4並列のメモリ共有探索に近い強さを実現した., Parallel game-tree search techniques on distributed systems have been intensively researched. However, one has to drastically modify his program to adopt such techniques. This paper presents a simple but effective framework of parallel game-tree search on distributed systems, which requires few modifications on a sequential implementation. In this framework, the master tree grows based on the prediction of the best moves at each internal node until when each slave is assigned a unique leaf in the master tree. Then, each slave independently conducts a game-tree search. In the experiments with GPS-Shogi, the presented search worked effectively even when one simply assigned most computer resources to the top two moves at each node. The strength of the presented search with 8 slaves was almost comparable to that of a shared memory search with 4-threads.},
 pages = {126--133},
 publisher = {情報処理学会},
 title = {最善手の予測に基づくゲーム木探索の分散並列実行},
 volume = {2010},
 year = {2010}
}