用C语言写一个返回值为落子坐标的六子棋博弈的alpha-beta剪枝算法
时间: 2023-06-26 09:03:15 浏览: 161
以下是一个简单的六子棋博弈的alpha-beta剪枝算法示例:
```c
#include <stdio.h>
#include <stdlib.h>
#define BOARD_SIZE 6
#define MAX_DEPTH 4
int board[BOARD_SIZE][BOARD_SIZE];
int get_score(int player) {
int score = 0;
// 计算当前玩家得分
return score;
}
int evaluate() {
int score1 = get_score(1);
int score2 = get_score(2);
return score1 - score2; // 返回当前局面的分数
}
int is_full() {
int i, j;
for (i = 0; i < BOARD_SIZE; i++) {
for (j = 0; j < BOARD_SIZE; j++) {
if (board[i][j] == 0) {
return 0;
}
}
}
return 1;
}
int is_gameover() {
// 检查是否有一方获胜或者棋盘已满
return 0;
}
int max_value(int alpha, int beta, int depth) {
if (depth == MAX_DEPTH || is_gameover()) {
return evaluate();
}
int i, j, score, max_score = -1000000;
for (i = 0; i < BOARD_SIZE; i++) {
for (j = 0; j < BOARD_SIZE; j++) {
if (board[i][j] == 0) {
board[i][j] = 1;
score = min_value(alpha, beta, depth + 1);
board[i][j] = 0;
if (score > max_score) {
max_score = score;
}
if (max_score >= beta) {
return max_score;
}
if (max_score > alpha) {
alpha = max_score;
}
}
}
}
return max_score;
}
int min_value(int alpha, int beta, int depth) {
if (depth == MAX_DEPTH || is_gameover()) {
return evaluate();
}
int i, j, score, min_score = 1000000;
for (i = 0; i < BOARD_SIZE; i++) {
for (j = 0; j < BOARD_SIZE; j++) {
if (board[i][j] == 0) {
board[i][j] = 2;
score = max_value(alpha, beta, depth + 1);
board[i][j] = 0;
if (score < min_score) {
min_score = score;
}
if (min_score <= alpha) {
return min_score;
}
if (min_score < beta) {
beta = min_score;
}
}
}
}
return min_score;
}
void alphabeta(int *row, int *col) {
int i, j, score, max_score = -1000000;
*row = -1;
*col = -1;
for (i = 0; i < BOARD_SIZE; i++) {
for (j = 0; j < BOARD_SIZE; j++) {
if (board[i][j] == 0) {
board[i][j] = 1;
score = min_value(-1000000, 1000000, 1);
board[i][j] = 0;
if (score > max_score) {
max_score = score;
*row = i;
*col = j;
}
}
}
}
}
int main() {
int i, j, row, col;
while (!is_gameover()) {
alphabeta(&row, &col);
board[row][col] = 1;
// AI 落子
// 玩家落子
}
return 0;
}
```
该算法通过递归搜索所有可能的落子位置,同时利用alpha-beta剪枝对搜索树进行剪枝,以提高算法效率。具体实现中,`max_value`函数表示AI的决策,`min_value`函数表示玩家的决策,`evaluate`函数计算当前局面的分数,`alphabeta`函数返回AI的落子位置。
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