function AI(grid) {
this.grid = grid;
}
// static evaluation function
AI.prototype.eval = function() {
var emptyCells = this.grid.availableCells().length;
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.monotonicity2() * mono2Weight
+ Math.log(emptyCells) * emptyWeight
+ this.grid.maxValue() * maxWeight;
};
//AI.prototype.cache = {}
// 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;
for (var direction in [0, 1, 2, 3]) {
var newGrid = this.grid.clone();
if (newGrid.move(direction).moved) {
positions++;
if (newGrid.isWin()) {
return { move: direction, score: 10000, positions: positions, cutoffs: cutoffs };
}
var newAI = new AI(newGrid);
if (depth == 0) {
result = { move: direction, score: newAI.eval() };
} else {
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;
}
if (result.score > bestScore) {
bestScore = result.score;
bestMove = direction;
}
if (bestScore > beta) {
cutoffs++
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 = [];
var cells = this.grid.availableCells();
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);
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
for (var i=0; i) {
if (scores[value][i] == maxScore) {
candidates.push( { position: cells[i], value: parseInt(value, 10) } );
}
}
}
// search on each candidate
for (var i=0; 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);
result = newAI.search(depth, alpha, bestScore, positions, cutoffs);
positions = result.positions;
cutoffs = result.cutoffs;
if (result.score < bestScore) {
bestScore = result.score;
}
if (bestScore < alpha) {
cutoffs++;
return { move: null, score: alpha, positions: positions, cutoffs: cutoffs };
}
}
}
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;
do {
var newBest = this.search(depth, -10000, 10000, 0 ,0);
if (newBest.move == -1) {
//console.log('BREAKING EARLY');
break;
} else {
best = newBest;
}
depth++;
} while ( (new Date()).getTime() - start < minSearchTime);
//console.log('depth', --depth);
//console.log(this.translate(best.move));
//console.log(best);
return best
}
AI.prototype.translate = function(move) {
return {
0: 'up',
1: 'right',
2: 'down',
3: 'left'
}[move];
}
Original: https://www.cnblogs.com/ryuasuka/p/3611839.html
Author: 飞鸟_Asuka
Title: [非原创]2048游戏自动化算法
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