暗棋中棋種間食物鏈關係之探討與實作
Abstract
電腦棋類一直是人工智慧發展的重要領域之一，而電腦暗棋至今仍較少人對其做較深入的研究。暗棋是屬於不完全資訊含機率性的棋類遊戲，不像西洋棋、象棋是屬於完全資訊的棋類遊戲，所以如果用一般遊戲樹進行搜尋，在走棋與翻棋夾雜的情況下，會因分枝度過大而無法做深入的搜尋，因此難以做出較佳的決策。
本論文希望改良先前謝曜安研究生的暗棋程式，首先改進他的走步生成方式，與審局函數的計算。由於他的審局函數是採用靜態子力去計算分數，不論盤面資訊如何，其各個子力價值恆為固定，在許多情況下會產生誤判，我們希望可以藉由盤面改變而動態的改變子力價值，更客觀小心的審視盤面，並以這審局函數來實作在暗棋中關於其棋種間特殊的食物鏈關係，以期加強暗棋程式的棋力程度，並使棋力超越人類玩家水平。
Computer chess is always an important research area in artificial intelligence. At present, there is less paper dealing with the playing strategies of Dark chess. Dark chess is an incomplete information game with probabilities, which is not the same as complete information games, such as chess or Chinese chess. If we use conventional game-tree searching techniques to tackle Dark chess, then the number of branches will be very large because there are lots of moves for both “dark pieces” and “bright pieces”. Hence, it is not easy to improve the strength of the Dark chess program by using the conventional game-tree searching techniques. This thesis is written to improve the Dark Chess program which was developed by the postgraduate Hsieh,Yao-An. We improve his move generator first, and then the evaluation function. As his evaluation function used static scores to calculate the materials’ values, regardless of how the chess game plays out, in many cases it will lead to wrong judgments. We want to dynamically change the chess materials scores when the chess board is changed to a more objective measurement. We carefully consider the unique food chain relations of the chess species and design a Dark Chess program to enhance the evaluation function. Finally, we combine several techniques to improve the strength of the program.
Computer chess is always an important research area in artificial intelligence. At present, there is less paper dealing with the playing strategies of Dark chess. Dark chess is an incomplete information game with probabilities, which is not the same as complete information games, such as chess or Chinese chess. If we use conventional game-tree searching techniques to tackle Dark chess, then the number of branches will be very large because there are lots of moves for both “dark pieces” and “bright pieces”. Hence, it is not easy to improve the strength of the Dark chess program by using the conventional game-tree searching techniques. This thesis is written to improve the Dark Chess program which was developed by the postgraduate Hsieh,Yao-An. We improve his move generator first, and then the evaluation function. As his evaluation function used static scores to calculate the materials’ values, regardless of how the chess game plays out, in many cases it will lead to wrong judgments. We want to dynamically change the chess materials scores when the chess board is changed to a more objective measurement. We carefully consider the unique food chain relations of the chess species and design a Dark Chess program to enhance the evaluation function. Finally, we combine several techniques to improve the strength of the program.
Description
Keywords
電腦暗棋, 不完全資訊, 人工智慧, artificial intelligence, Dark chess, incomplete information game