针对2048游戏，有人实现了一个AI程序，可以以较大概率（高于90%）赢得游戏，并且作者在
*上简要介绍了AI的算法框架和实现思路。
其中算法的主要是在ai.js文件中。我加了很多的注释在其中，方便大家理解。
function AI(grid) {
this.grid = grid;
}
// static evaluation function
AI.prototype.eval = function() {
var emptyCells = this.grid.availableCells().length;
//各种计算的权重，可以自己手动的调试
var smoothWeight = 0.1,
//monoWeight = 0.0,
//islandWeight = 0.0,
mono2Weight = 1.0,
emptyWeight = 2.7,
maxWeight = 1.0;
return this.grid.smoothness() * smoothWeight
//+ this.grid.monotonicity() * monoWeight
//- this.grid.islands() * islandWeight
+ this.grid.monotonicity2() * mono2Weight
+ Math.log(emptyCells) * emptyWeight
+ this.grid.maxValue() * maxWeight;
};
// alpha-beta depth first search
AI.prototype.search = function(depth, alpha, beta, positions, cutoffs) {
var bestScore;
var bestMove = -1;
var result;
// the maxing player
if (this.grid.playerTurn) {
bestScore = alpha;
//遍历四个方向
for (var direction in [0, 1, 2, 3]) {
var newGrid = this.grid.clone();
//逐一搜寻方向
if (newGrid.move(direction).moved) {
positions++;
//如果已经赢了直接返回
if (newGrid.isWin()) {
return { move: direction, score: 10000, positions: positions, cutoffs: cutoffs };
}
//如果还没有赢得话就继续想下走
var newAI = new AI(newGrid);
//当深度为0的时候停止
if (depth == 0) {
result = { move: direction, score: newAI.eval() };
} else {//继承父节点的alpha
//递归的搜寻 注意每移动一次轮次翻转 player->computer->player->computer->....
result = newAI.search(depth-1, bestScore, beta, positions, cutoffs);
if (result.score > 9900) { // win
result.score--; // to slightly penalize higher depth from win
}
positions = result.positions;
cutoffs = result.cutoffs;
}
//如果返回的分数>当前最好分数则给bestScore和bestMove重新赋值
if (result.score > bestScore) {
bestScore = result.score;
bestMove = direction;
}
//如果最好的分数大于beta也即意味着上一层节点不会继续向下走 切分
if (bestScore > beta) {
cutoffs++
//既然不会往这儿走，那么分数还是你上层的beta
return { move: bestMove, score: beta, positions: positions, cutoffs: cutoffs };
}
}
}
}
else { // computer's turn, we'll do heavy pruning to keep the branching factor low
bestScore = beta;
// try a 2 and 4 in each cell and measure how annoying it is
// with metrics from eval
var candidates = [];
//得到可以填充块的坐标
var cells = this.grid.availableCells();
//分数为2 或者 4
var scores = { 2: [], 4: [] };
for (var value in scores) {
for (var i in cells) {
scores[value].push(null);
var cell = cells[i];
var tile = new Tile(cell, parseInt(value, 10));
this.grid.insertTile(tile);
//算出分数，下面的计算可以看出要算出分数最大的，我们知道min节点是使游戏变得更难
//那么smoothness要越小越好，所以加上符号(越大越好) islands意味着有数字的格子，当然越多越好
//通俗理解就是不能合并在一起的越多越好
scores[value][i] = -this.grid.smoothness() + this.grid.islands();
this.grid.removeTile(cell);
}
}
// now just pick out the most annoying moves
var maxScore = Math.max(Math.max.apply(null, scores[2]), Math.max.apply(null, scores[4]));
for (var value in scores) { // 2 and 4
//将最大分数的候选者选出来（满足最大分数的可能不止一个）
for (var i=0; i<scores[value].length; i++) {
if (scores[value][i] == maxScore) {
candidates.push( { position: cells[i], value: parseInt(value, 10) } );
}
}
}
// search on each candidate
for (var i=0; i<candidates.length; i++) {
var position = candidates[i].position;
var value = candidates[i].value;
var newGrid = this.grid.clone();
var tile = new Tile(position, value);
newGrid.insertTile(tile);
newGrid.playerTurn = true;
positions++;
newAI = new AI(newGrid);
//min节点往下的alpha还是上层的，beta是最好的分数，也就是说下层节点如果你取得的最大值只能是beta，
//如果大于beta我会把你pass掉
result = newAI.search(depth, alpha, bestScore, positions, cutoffs);
positions = result.positions;
cutoffs = result.cutoffs;
//竟然有比beta还小的分数，好，我选择你
if (result.score < bestScore) {
bestScore = result.score;
}
//如果最好的分数小于上层的下界 意味着上层节点肯定不会继续向下走
if (bestScore < alpha) {
cutoffs++;
//返回的分数是还是上层的值也就是说你反正不会走我这条路，那你的分数还是你原来的分数
//关于此处的move:null 注意上面的result的depth是继承父节点的，意味着depth>0
//也就是说最后一步必为max节点，min节点是不会有move操作的，所以直接返回null
return { move: null, score: alpha, positions: positions, cutoffs: cutoffs };
}
}
}
//计算到最好返回这些计算的值
return { move: bestMove, score: bestScore, positions: positions, cutoffs: cutoffs };
}
// performs a search and returns the best move
AI.prototype.getBest = function() {
return this.iterativeDeep();
}
// performs iterative deepening over the alpha-beta search
AI.prototype.iterativeDeep = function() {
var start = (new Date()).getTime();
var depth = 0;
var best;
//没有规定固定的depth 而是规定了计算时间，在规定时间内能计算到的深度
do {
var newBest = this.search(depth, -10000, 10000, 0 ,0);
if (newBest.move == -1) {
break;
} else {
best = newBest;
}
depth++;
} while ( (new Date()).getTime() - start < minSearchTime);
return best
}
AI.prototype.translate = function(move) {
return {
0: 'up',
1: 'right',
2: 'down',
3: 'left'
}[move];
}
以上就是根据我的理解所做的注释，供大家参考如果有错误请大家指正！