添加slug，改版文章目录命名结构，添加了一个实用的新建文章Shell脚本。
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// 亚马逊棋（Amazons）蒙特卡洛AI程序 - 优化版
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#include <iostream>
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#include <string>
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#include <cstdlib>
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#include <ctime>
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#include <vector>
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#include <cmath>
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#include <algorithm>
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#include <random>
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#include <chrono>
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#include <queue>
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#include <cstring> // 添加memset
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#define GRIDSIZE 8
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#define OBSTACLE 2
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#define judge_black 0
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#define judge_white 1
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#define grid_black 1
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#define grid_white -1
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using namespace std;
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// 均衡型参数配置
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const double CONTROL_FACTOR = 1.0;
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const double SAFETY_FACTOR = 1.0;
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const double SURROUND_FACTOR = 10.0;
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const double CENTER_FACTOR = 1.0;
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// 蒙特卡洛参数 - 减少模拟次数提高速度
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const int SIMULATIONS_PER_MOVE = 15; // 减少模拟次数
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const int MAX_SIMULATION_STEPS = 5; // 减少模拟步数
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const int MCTS_ITERATIONS = 50; // 减少迭代次数
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// 预计算方向向量
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const int dx[8] = { -1, -1, -1, 0, 0, 1, 1, 1 };
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const int dy[8] = { -1, 0, 1, -1, 1, -1, 0, 1 };
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int currBotColor;
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int gridInfo[GRIDSIZE][GRIDSIZE] = { 0 };
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// 随机数生成器
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static mt19937 rng(chrono::steady_clock::now().time_since_epoch().count());
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// 走法结构体 - 紧凑表示
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struct Move {
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unsigned char x0, y0, x1, y1, x2, y2;
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Move(int x0=0, int y0=0, int x1=0, int y1=0, int x2=0, int y2=0)
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: x0(x0), y0(y0), x1(x1), y1(y1), x2(x2), y2(y2) {}
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bool isValid() const { return x0 != 255; }
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};
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// 移动缓存结构
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struct MoveCache {
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int pieceX, pieceY;
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int moveCount;
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int moves[56]; // 最大可能移动数：8个方向 * 7步 = 56
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MoveCache() : pieceX(-1), pieceY(-1), moveCount(0) {
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memset(moves, 0, sizeof(moves));
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}
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};
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// 预计算中心距离
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const double centerDistance[GRIDSIZE][GRIDSIZE] = {
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{9.90, 8.96, 8.06, 7.28, 7.28, 8.06, 8.96, 9.90},
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{8.96, 7.07, 6.10, 5.39, 5.39, 6.10, 7.07, 8.96},
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{8.06, 6.10, 4.95, 4.03, 4.03, 4.95, 6.10, 8.06},
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{7.28, 5.39, 4.03, 2.12, 2.12, 4.03, 5.39, 7.28},
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{7.28, 5.39, 4.03, 2.12, 2.12, 4.03, 5.39, 7.28},
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{8.06, 6.10, 4.95, 4.03, 4.03, 4.95, 6.10, 8.06},
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{8.96, 7.07, 6.10, 5.39, 5.39, 6.10, 7.07, 8.96},
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{9.90, 8.96, 8.06, 7.28, 7.28, 8.06, 8.96, 9.90}
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};
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// 全局移动列表缓存
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vector<Move> allPossibleMoves;
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vector<MoveCache> moveCache[2]; // 0:黑方, 1:白方
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inline bool inMap(int x, int y) {
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return (unsigned)x < GRIDSIZE && (unsigned)y < GRIDSIZE;
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}
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// 快速棋盘复制（使用memcpy）
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inline void copyBoard(const int src[GRIDSIZE][GRIDSIZE], int dst[GRIDSIZE][GRIDSIZE]) {
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memcpy(dst, src, sizeof(int) * GRIDSIZE * GRIDSIZE);
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}
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bool ProcStep(int x0, int y0, int x1, int y1, int x2, int y2, int color, bool check_only) {
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if (!inMap(x0, y0) || !inMap(x1, y1) || !inMap(x2, y2))
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return false;
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if (gridInfo[x0][y0] != color || gridInfo[x1][y1] != 0)
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return false;
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if (gridInfo[x2][y2] != 0 && !(x2 == x0 && y2 == y0))
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return false;
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if (!check_only) {
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gridInfo[x0][y0] = 0;
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gridInfo[x1][y1] = color;
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gridInfo[x2][y2] = OBSTACLE;
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}
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return true;
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}
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// 计算棋子的所有移动位置（优化版）
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void calculateMovesFast(const int grid[GRIDSIZE][GRIDSIZE], int x, int y,
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int moves[], int &count) {
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count = 0;
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int color = grid[x][y];
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if (color == 0) return;
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for (int d = 0; d < 8; ++d) {
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for (int step = 1; step < GRIDSIZE; ++step) {
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int xx = x + dx[d] * step;
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int yy = y + dy[d] * step;
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if (!inMap(xx, yy) || grid[xx][yy] != 0)
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break;
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moves[count++] = (xx << 3) | yy; // 使用位运算压缩坐标
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}
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}
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}
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// 预先计算所有棋子的移动
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void precalculateMoves(const int grid[GRIDSIZE][GRIDSIZE], int color) {
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int idx = (color == grid_black) ? 0 : 1;
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moveCache[idx].clear();
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for (int i = 0; i < GRIDSIZE; ++i) {
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for (int j = 0; j < GRIDSIZE; ++j) {
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if (grid[i][j] == color) {
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MoveCache mc;
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mc.pieceX = i;
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mc.pieceY = j;
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calculateMovesFast(grid, i, j, mc.moves, mc.moveCount);
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if (mc.moveCount > 0) {
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moveCache[idx].push_back(mc);
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}
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}
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}
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}
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}
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// 获取所有可能的走法（使用缓存）
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vector<Move> getAllPossibleMovesFast(const int grid[GRIDSIZE][GRIDSIZE], int color) {
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vector<Move> moves;
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int idx = (color == grid_black) ? 0 : 1;
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// 如果缓存未计算，先计算
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if (moveCache[idx].empty()) {
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precalculateMoves(grid, color);
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}
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// 临时棋盘用于计算箭的位置
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int tempGrid[GRIDSIZE][GRIDSIZE];
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for (const auto& piece : moveCache[idx]) {
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for (int m = 0; m < piece.moveCount; ++m) {
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int movePos = piece.moves[m];
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int moveX = movePos >> 3;
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int moveY = movePos & 7;
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// 创建临时棋盘
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copyBoard(grid, tempGrid);
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tempGrid[moveX][moveY] = color;
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tempGrid[piece.pieceX][piece.pieceY] = 0;
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// 计算箭的位置
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int arrowMoves[56];
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int arrowCount = 0;
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calculateMovesFast(tempGrid, moveX, moveY, arrowMoves, arrowCount);
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for (int a = 0; a < arrowCount; ++a) {
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int arrowPos = arrowMoves[a];
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int arrowX = arrowPos >> 3;
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int arrowY = arrowPos & 7;
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moves.push_back(Move(piece.pieceX, piece.pieceY, moveX, moveY, arrowX, arrowY));
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}
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}
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}
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return moves;
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}
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// 快速局面评估（简化版）
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double evaluateBoardFast(const int grid[GRIDSIZE][GRIDSIZE], int originalColor) {
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double score = 0.0;
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// 快速评估：只计算移动能力和中心控制
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for (int i = 0; i < GRIDSIZE; ++i) {
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for (int j = 0; j < GRIDSIZE; ++j) {
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int piece = grid[i][j];
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if (piece != 0 && piece != OBSTACLE) {
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// 中心控制
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double centerValue = (10.0 - centerDistance[i][j]) * CENTER_FACTOR;
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// 移动能力（简化计算）
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int moves[56];
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int moveCount = 0;
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calculateMovesFast(grid, i, j, moves, moveCount);
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double mobility = moveCount * CONTROL_FACTOR;
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if (piece == originalColor) {
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score += centerValue + mobility;
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} else {
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score -= centerValue + mobility;
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}
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}
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}
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}
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// 归一化
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return tanh(score / 50.0);
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}
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// 优化的随机游戏模拟
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double simulateRandomGameFast(Move move, const int originalGrid[GRIDSIZE][GRIDSIZE], int color) {
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// 使用栈上数组，避免动态分配
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int simGrid[GRIDSIZE][GRIDSIZE];
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copyBoard(originalGrid, simGrid);
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// 执行第一步
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simGrid[move.x1][move.y1] = color;
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simGrid[move.x0][move.y0] = 0;
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simGrid[move.x2][move.y2] = OBSTACLE;
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int currentColor = -color;
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int steps = 0;
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// 快速随机模拟
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while (steps < MAX_SIMULATION_STEPS) {
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// 获取当前玩家的移动
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int moves[56];
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int moveCount = 0;
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bool hasMove = false;
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// 找到一个可以移动的棋子
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for (int i = 0; i < GRIDSIZE && !hasMove; ++i) {
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for (int j = 0; j < GRIDSIZE && !hasMove; ++j) {
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if (simGrid[i][j] == currentColor) {
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calculateMovesFast(simGrid, i, j, moves, moveCount);
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if (moveCount > 0) {
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// 随机选择一个移动
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int moveIdx = rng() % moveCount;
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int movePos = moves[moveIdx];
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int moveX = movePos >> 3;
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int moveY = movePos & 7;
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// 计算箭的位置
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int arrowMoves[56];
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int arrowCount = 0;
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// 临时棋盘计算箭
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int tempGrid[GRIDSIZE][GRIDSIZE];
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copyBoard(simGrid, tempGrid);
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tempGrid[moveX][moveY] = currentColor;
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tempGrid[i][j] = 0;
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calculateMovesFast(tempGrid, moveX, moveY, arrowMoves, arrowCount);
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if (arrowCount > 0) {
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int arrowIdx = rng() % arrowCount;
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int arrowPos = arrowMoves[arrowIdx];
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int arrowX = arrowPos >> 3;
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int arrowY = arrowPos & 7;
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// 执行移动
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simGrid[moveX][moveY] = currentColor;
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simGrid[i][j] = 0;
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simGrid[arrowX][arrowY] = OBSTACLE;
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hasMove = true;
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}
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}
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}
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}
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}
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if (!hasMove) break;
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currentColor = -currentColor;
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steps++;
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}
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return evaluateBoardFast(simGrid, color);
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}
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// 主决策函数
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Move findBestMoveWithMonteCarlo() {
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// 清空缓存
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moveCache[0].clear();
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moveCache[1].clear();
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// 获取所有可能走法
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vector<Move> moves = getAllPossibleMovesFast(gridInfo, currBotColor);
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if (moves.empty()) {
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return Move(255, 255, 255, 255, 255, 255);
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}
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// 如果走法很少，直接返回第一个
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if (moves.size() == 1) {
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return moves[0];
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}
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Move bestMove = moves[0];
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double bestScore = -1e9;
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// 限制检查的走法数量
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int checkCount = min((int)moves.size(), 30);
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// 均匀采样，而不是检查所有走法
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vector<int> indices(moves.size());
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for (int i = 0; i < moves.size(); ++i) indices[i] = i;
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shuffle(indices.begin(), indices.end(), rng);
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for (int i = 0; i < checkCount; ++i) {
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int idx = indices[i];
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double totalScore = 0.0;
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// 减少模拟次数
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int sims = (i < 10) ? SIMULATIONS_PER_MOVE : (SIMULATIONS_PER_MOVE / 2);
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for (int sim = 0; sim < sims; ++sim) {
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totalScore += simulateRandomGameFast(moves[idx], gridInfo, currBotColor);
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}
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double avgScore = totalScore / sims;
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if (avgScore > bestScore) {
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bestScore = avgScore;
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bestMove = moves[idx];
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}
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}
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return bestMove;
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}
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int main() {
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ios::sync_with_stdio(false);
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cin.tie(0);
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// 快速初始化棋盘
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memset(gridInfo, 0, sizeof(gridInfo));
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gridInfo[0][2] = gridInfo[2][0] = gridInfo[5][0] = gridInfo[7][2] = grid_black;
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gridInfo[0][5] = gridInfo[2][7] = gridInfo[5][7] = gridInfo[7][5] = grid_white;
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int turnID;
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cin >> turnID;
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currBotColor = grid_white;
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for (int i = 0; i < turnID; i++) {
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int x0, y0, x1, y1, x2, y2;
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cin >> x0 >> y0 >> x1 >> y1 >> x2 >> y2;
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if (x0 == -1) {
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currBotColor = grid_black;
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} else if (x0 >= 0) {
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ProcStep(x0, y0, x1, y1, x2, y2, -currBotColor, false);
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}
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if (i < turnID - 1) {
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cin >> x0 >> y0 >> x1 >> y1 >> x2 >> y2;
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if (x0 >= 0) {
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ProcStep(x0, y0, x1, y1, x2, y2, currBotColor, false);
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}
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}
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}
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// 决策
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Move bestMove = findBestMoveWithMonteCarlo();
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// 如果无效，使用简单策略
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if (!bestMove.isValid()) {
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// 简单策略：找到第一个合法走法
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vector<Move> moves = getAllPossibleMovesFast(gridInfo, currBotColor);
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if (!moves.empty()) {
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bestMove = moves[0];
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}
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}
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cout << (int)bestMove.x0 << ' ' << (int)bestMove.y0 << ' '
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<< (int)bestMove.x1 << ' ' << (int)bestMove.y1 << ' '
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<< (int)bestMove.x2 << ' ' << (int)bestMove.y2 << endl;
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return 0;
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}
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