標題: | 適用於象棋開局庫之工作層級極小極大化搜尋 Job-Level Minmax Search for Chinese Chess Opening Book |
作者: | 甘崇緯 Kan, Chung-Wei 吳毅成 Wu, I-Chen 多媒體工程研究所 |
關鍵字: | 工作層級;極小極大化搜尋;中國象棋;象棋;開局庫;電腦遊戲;桌機格網系統;志願型計算系統;Job-Level;Minmax Search;Minimax Search;Chinese Chess;Opening Book;Opening;SSS*;DUAL*;CGDG;Computer Game;Desktop Grid;Volunteer Computing System |
公開日期: | 2011 |
摘要: | 本論文的目的在於找到一個快速驗證開局庫是否適合AI程式的方式,使得程式編寫者在改變開局庫資料及AI程式時,不用擔心需要花許多時間在銜接開局庫與AI程式的搭配上。我們進一步提出幾種可增進效能的策略,並且實驗比較何種策略在驗證開局庫上能增進較多的效能。
根據我們的實驗顯示,本論文提出的job-level SSS* opening確實能更快速驗證象棋開局庫,且更新所有父節點跟有限度的中止工作能更進一步增進效能,達到本論文的目的。 The main goal of this thesis is to find a quick way to verify if an opening is suitable for an AI program or not. The programmer would spend less time on changing the opening data and the AI program while merging opening into the AI program. Furthermore, we propose some strategies that could enhance the effectiveness and find the one of which benefits much. According to our experiment, this thesis showed that the job-level SSS* opening method performs more quickly than other methods. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT079857507 http://hdl.handle.net/11536/48428 |
Appears in Collections: | Thesis |
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