近年來關於人工智慧的研究如火如荼的湧現，未來電腦的效能越高，相同時間內能處理的資訊量也會越高，人工智慧能解決的問題複雜程度亦會越來越大，例如近年來電腦對局中圍棋與人工智慧的成功結合例子AlphaGo。然而電腦對局的研究若要進展得順利，設計良善的使用者操作介面必不可少，所以本研究針對中國跳棋遊戲設計一個支援多玩家遊戲的自動對弈平台，期望在電腦對局的發展上能貢獻微薄之力。

本研究開發之電腦中國跳棋自動對弈平台利用OpenCV函式庫實作出一個直觀且方便的圖形化使用者操作介面，可以只用滑鼠點選各項功能進行設定並操作棋子。另外平台具備程式偵錯與對戰等主要功能，除了在比賽時能夠使雙方更清楚地檢視遊戲過程並減少手動操作失誤與爭議之外，偵錯時也可以藉由此平台檢視與驗證每一個走步的正確性，預設對戰對手也包含了本研究開發之AI引擎提供開發者測試強度。如此一來其他跳棋研究者只需專注於研發跳棋程式的AI引擎，以期望有更多同好加入電腦中國跳棋的研究，也有助於推廣中國跳棋這項遊戲。

In recent years, research on artificial intelligence has emerged in full swing. The higher efficiency of computers, the more information that can be processed in the same period of time. The complexity of the problems that artificial intelligence can solve will also become more and more complex in the future. A successful example is the combination of Go and artificial intelligence in the program named AlphaGo. However, if the computer game research is going smoothly, the well-designed user interface is necessary, so this study designed an automatic game platform for multi-player games for Chinese Checkers. We hope this platform can help any others who want to develop Chinese Checkers.

The platform uses the OpenCV library to make an intuitive and convenient graphical user interface. We can use the mouse to operate the game easily. The platform has the main functions such as program debugging and battle with multiplayers. In addition to view the game clearly and reduce manual errors during the game, the platform can also verify whether each move is valid or not. The platform also includes AI for other developers to test, so the developers only need to focus on the AI engine of the Chinese Checkers program. We are not only expecting more researchers to join, but also we are promoting the Chinese Checkers game.

[1] 中國跳棋，https://zh.wikipedia.org/wiki/%E4%B8%AD%E5%9C%8B%E8%B7%B3%E6%A3%8B。
[2] 跳棋，https://blog.xuite.net/strong98009/twblog1/178250552-%E8%B7%B3%E6%A3%8B。
[3] 蒙地卡羅樹搜尋，https://zh.wikipedia.org/zh-tw/%E8%92%99%E7%89%B9%E5%8D%A1%E6%B4%9B%E6%A0%91%E6%90%9C%E7%B4%A2。
[4] Sijun He ,Wenjie ,Hu Hao Yin , “Playing Chinese Checkers with Reinforcement Learning”, CS 229 Spring stanford ,2016.
[5] AlphaGo，https://zh.wikipedia.org/wiki/AlphaGo。
[6] DeepMind , “AlphaGo Zero -- Mastering the Game of Go without Human Knowledge ”, 5 New Street Square, London,2017.
[7] Install TensorFlow for C，https://www.tensorflow.org/install/lang_c?hl=en-us
[8] 2018.12.04- 千呼萬喚終於始出來！！OpenCV 4.0發佈，https://blog.cavedu.com/2018/12/04/%E4%BB%8B%E7%B4%B9%E6%96%87-2018-12-04-opencv-4-0%E7%99%BC%E4%BD%88。
[9] github網站 -- cvui，https://github.com/Dovyski/cvui。
[10] KGS Go Server首頁，https://www.gokgs.com/。
[11] 網路象棋自動對弈系統下載網址，http://web.ntpu.edu.tw/~jcchen/clients/ChineseChess_V3.1_ICGA_2016.zip。
[12] 暗棋對弈平台下載網址，http://web.ntpu.edu.tw/~jcchen/clients/CDC_package_windows.zip。
[13] 電腦愛因斯坦棋自動對弈平台下載網址，https://www.tcga.tw/icga-computer-olympiad-2018/attachment/EinStein_w%C3%BCrfelt_nicht_platform.rar。
[14] 電腦對局集成對戰平台下載網址，http://computergames.caai.cn/platform/SAU_Game_Platform_2.1.0_r4.zip。
[15] N. A. Ojekudo. N. P. Akpan, “An anapplication of Dijkstra’s Algorithm to shortest route problem.,” IOSR Journal of Mathematics Volume 13, Issue 3 Ver. 1, pp. 20-32, 5 2017.
[16] Alpha Zero General開源框架下載網址，
https://github.com/suragnair/alpha-zero-general。
[17] DeepMind , “AI Programming (IT-3105) Project Module # 4: Deep Reinforcement Learning for Game Playing” ，https://www.idi.ntnu.no/emner/it3105/materials/neural/deep-rl-pegs.pdf.
[18] Chen, C.-H. Wu, W.-L., Chen, Y.-H. and Lin, S.-S. (2018). Some Improvements in Monte Carlo Tree Search Algorithms for Sudden Death Games. The 10th International Conference on Computers and Games (Taipei, Taiwan, July 9-11, 2018).
[19] github網站 -- ChineseCheckersGUI_v2，https://github.com/phoenixchen/ChineseCheckersGUI_v2。
[20] 座標平面上的旋轉變換，http://highscope.ch.ntu.edu.tw/wordpress/?p=51646。