在我的方法newminimax49中，我有一个minimax算法，该算法利用了memoization以及在post中向我建议的其他一般改进。该方法使用简单的启发式板评估功能。我的问题基本上是关于alpha beta修剪，即我的minimax方法是否使用alpha beta修剪。据我所知，我相信它确实如此，但是我过去常常实现它似乎太简单了。此外，其他人建议我使用alpha beta修剪，正如我所说，我曾经认为我的minimax方法已经做了，这让我相信我在这里所做的是别的东西。所以这是我的newminimax49：
//This method returns a 2 element int array containing the position of the best possible
//next move and the score it yields. Utilizes memoization and supposedly alpha beta
//pruning to achieve better performance. Alpha beta pruning can be seen in lines such as:
/*if(bestScore==-10)
break;*/
//This basically means that if the best score achieved is the best possible score
//achievable then stop exploring the other available moves. Doing thing I believe
//I'm applying the same principle of alpha beta pruning.
public int[] newminimax49(){
int bestScore = (turn == 'O') ? +9 : -9; //X is minimizer, O is maximizer
int bestPos=-1;
int currentScore;
//boardShow();
String stateString = "";
for (int i=0; i<state.length; i++)
stateString += state[i];
int[] oldAnswer = oldAnswers.get(stateString);
if (oldAnswer != null)
return oldAnswer;
if(isGameOver2()!='N'){
//s.boardShow();
bestScore= score();
}
else{
//s.boardShow();
int i=0;
for(int x:getAvailableMoves()){
if(turn=='X'){ //X is minimizer
setX(x);
//boardShow();
//System.out.println(stateID++);
currentScore = newminimax49()[0];
revert(x);
if(i==0){
bestScore = currentScore;
bestPos=x;
if(bestScore==-10)
break;
}
else if(currentScore<bestScore){
bestScore = currentScore;
bestPos=x;
if(bestScore==-10)
break;
}
}
else { //O is maximizer
setO(x);
//boardShow();
//System.out.println(stateID++);
currentScore = newminimax49()[0];
revert(x);
//boardShow();
if(i==0){
bestScore = currentScore;
bestPos=x;
if(bestScore==10)
break;
}
else if(currentScore>bestScore){
bestScore = currentScore;
bestPos = x;
if(bestScore==10)
break;
}
}
i++;
}
}
int[] answer = {bestScore, bestPos};
oldAnswers.put (stateString, answer);
return answer;
}
我的类State2中使用的字段和构造函数：
private char [] state; //Actual content of the board
private char turn; //Whose turn it is
private Map<String,int[]> oldAnswers; //Used for memoization. It saves every state along with the score it yielded which allows us to stop exploring the children of a certain node if a similar node's score has been previously calculated. The key is the board state(i.e OX------X for example), the int array is a 2 element array containing the score and position of last placed seed of the state.
private Map<Integer, int []> RowCol; //A mapping of positions from a board represented as a normal array to a board represented as a 2d array. For example: The position 0 maps to 0,0 on a 2d array board, 1 maps to 0,1 and so on.
private static int n; //Size of the board
private static int stateID; //An simple incrementer used to show number of recursive calls in the newminiax49 method.
private static int countX, countO; //Number of placed Xs and Os
private static int lastAdded; //Position of last placed seed
private char [][] DDState; //A 2d array representing the board. Contains the same values as state[]. Used for simplicity in functions that check the state of the board.
public State2(int n){
int a=0;
State2.n=n;
state=new char[n*n];
RowCol=new HashMap<Integer, int []>();
countX=0;
countO=0;
//Initializing the board with empty slots
for(int i = 0; i<state.length; i++){
state[i]='-';
}
//Mapping
for(int i=0; i<n; i++){
for(int j=0; j<n; j++){
RowCol.put(a, new int[]{i, j});
a++;
}
}
a=0;
DDState=new char[n][n];
//Initializing the 2d array with the values from state[](empty slots)
for(int i=0; i<n; i++){
for(int j=0; j<n; j++){
DDState[i][j]=state[a];
a++;
}
}
oldAnswers = new HashMap<String,int[]>();
}
补充方法：
getAvailableMoves，返回一个板上有空插槽的数组（即可能的下一步移动）。
public int[] getAvailableMoves(){
int count=0;
int i=0;
for(int j=0; j<state.length; j++){
if(state[j]=='-')
count++;
}
int [] availableSlots = new int[count];
for(int j=0; j<state.length; j++){
if(state[j]=='-')
availableSlots[i++]=j;
}
return availableSlots;
}
isGameOver2（），只需检查棋盘的当前状态是否游戏结束。返回一个字符'X'，'O'，'D'和'N'，分别代表X赢，O赢，抽奖和不游戏。
public char isGameOver2(){
char turnOpp;
int count;
if(turn=='X'){
count=countO;
turnOpp='O';
}
else {
count=countX;
turnOpp='X';
}
if(count>=n){
//^No win available if each player has less than n seeds on the board
//Checking begins
//DDState[RowCol.get(lastAdded)[0]][RowCol.get(lastAdded)[1]]=turn;
//Check column for win
for(int i=0; i<n; i++){
if(DDState[i][RowCol.get(lastAdded)[1]]!=turnOpp)
break;
if(i==(n-1)){
//DDState[RowCol.get(x)[0]][RowCol.get(x)[1]]='-';
return turnOpp;
}
}
//Check row for win
for(int i=0; i<n; i++){
if(DDState[RowCol.get(lastAdded)[0]][i]!=turnOpp)
break;
if(i==(n-1)){
//DDState[RowCol.get(x)[0]][RowCol.get(x)[1]]='-';
return turnOpp;
}
}
//Check diagonal for win
if(RowCol.get(lastAdded)[0] == RowCol.get(lastAdded)[1]){
//we're on a diagonal
for(int i = 0; i < n; i++){
if(DDState[i][i] != turnOpp)
break;
if(i == n-1){
//DDState[RowCol.get(x)[0]][RowCol.get(x)[1]]='-';
return turnOpp;
}
}
}
//check anti diagonal
for(int i = 0; i<n; i++){
if(DDState[i][(n-1)-i] != turnOpp)
break;
if(i == n-1){
//DDState[RowCol.get(x)[0]][RowCol.get(x)[1]]='-';
return turnOpp;
}
}
//check for draw
if((countX+countO)==(n*n))
return 'D';
}
return 'N';
}
boardShow，返回电路板当前状态的矩阵显示：
public void boardShow(){
if(n==3){
System.out.println(stateID);
for(int i=0; i<=6;i+=3)
System.out.println("["+state[i]+"]"+" ["+state[i+1]+"]"+" ["+state[i+2]+"]");
System.out.println("***********");
}
else {
System.out.println(stateID);
for(int i=0; i<=12;i+=4)
System.out.println("["+state[i]+"]"+" ["+state[i+1]+"]"+" ["+state[i+2]+"]"+" ["+state[i+3]+"]");
System.out.println("***********");
}
}
得分，是一个简单的评价函数，其中O赢取+10，X赢取-10，抽奖0：
public int score(){
if(isGameOver2()=='X')
return -10;
else if(isGameOver2()=='O')
return +10;
else
return 0;
}
种子制定者：
//Sets an X at a certain location and updates the turn, countX and lastAdded variables
public void setX(int i){
state[i]='X';
DDState[RowCol.get(i)[0]][RowCol.get(i)[1]]='X';
turn='O';
countX++;
lastAdded=i;
}
//Sets an O at a certain location and updates the turn, countO and lastAdded variables
public void setO(int i){
state[i]='O';
DDState[RowCol.get(i)[0]][RowCol.get(i)[1]]='O';
turn='X';
countO++;
lastAdded=i;
}
还原，只需恢复移动。例如，如果X已被放置在位置0，则revert（0）在其位置设置' - '并更新由setX更改的变量：
public void revert(int i){
state[i]='-';
DDState[RowCol.get(i)[0]][RowCol.get(i)[1]]='-';
if(turn=='X'){
turn = 'O';
countO--;
}
else {
turn = 'X';
countX--;
}
}
对于你们来说，这看起来像alpha beta修剪，如果不是，我怎么能实现呢？
答案 0 :(得分：1)
您已经在使用某种“简化”Alpha-Beta：目前，只要玩家找到获胜位置，您就会进行修剪。
一个合适的AB会传递一个Alpha和一个Beta值，以确定玩家将达到的最小值。在那里，只要得分比对方球员当前的“最坏情况”更糟或更差，你就会修剪。
在你的情况下，你不仅可以修正获胜分数（就像你现在那样），而且还可以修正一些0分。