使用Alpha-Beta修剪的AI问题 - Java

时间:2016-04-16 17:30:18

标签: java algorithm artificial-intelligence alpha-beta-pruning iterative-deepening

我正在参加一个人工智能课程,我们需要创建一个玩Tic-Tac-Toe的AI。

教师指定在进行下一步动作时使用alpha-beta修剪来完成AI的决策过程。我此时遇到的问题是AI创建决策树并进行移动所需的时间。普通3x3很好,3x4和4x3需要一点时间,但4x4需要多分钟才能完成第一步,而且我还没有得到比这更大的游戏板的结果。

我使用的源代码:

  /** Get next best move for computer. Return int[2] of {row, >col} */ 
  @Override 
  int[] move() {
     int[] result = minimax(2, mySeed, Integer.MIN_VALUE, >Integer.MAX_VALUE);
        // depth, max-turn, alpha, beta
     return new int[] {result[1], result[2]};   // row, col
  }

  /** Minimax (recursive) at level of depth for maximizing or >minimizing player
      with alpha-beta cut-off. Return int[3] of {score, row, col}  >*/    
  private int[] minimax(int depth, Seed player, int alpha, int >beta) {
     // Generate possible next moves in a list of int[2] of {row, >col}.
     List<int[]> nextMoves = generateMoves();

     // mySeed is maximizing; while oppSeed is minimizing
     int score;
     int bestRow = -1;
     int bestCol = -1;

     if (nextMoves.isEmpty() || depth == 0) {
        // Gameover or depth reached, evaluate score
        score = evaluate();
        return new int[] {score, bestRow, bestCol};
     } else {
        for (int[] move : nextMoves) {
           // try this move for the current "player"
           cells[move[0]][move[1]].content = player;
           if (player == mySeed) {  // mySeed (computer) is >maximizing player
              score = minimax(depth - 1, oppSeed, alpha, beta)[0];
              if (score > alpha) {
                 alpha = score;
                 bestRow = move[0];
                 bestCol = move[1];

              }
           } else {  // oppSeed is minimizing player
              score = minimax(depth - 1, mySeed, alpha, beta)[0];
              if (score < beta) {
                 beta = score;
                 bestRow = move[0];
                 bestCol = move[1];
              }
           }
           // undo move
           cells[move[0]][move[1]].content = Seed.EMPTY;
           // cut-off
           if (alpha >= beta) break;
        }
        return new int[] {(player == mySeed) ? alpha : beta, >bestRow, bestCol};
     }
 }
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教师还建议使用迭代加深搜索,但我是一个不知道如何的假人。

0 个答案:

没有答案