autoplayer upgraded 2 moves ahead

src/backend/AutoPlayer.java

@@ -4,13 +4,69 @@ import java.util.ArrayList;
 
 public class AutoPlayer {
 
+    private static final int MAX_DEPTH = 2; // You can increase this for stronger AI
+
     public Move computeBestMove(Board board) {
+        return minimax(board, MAX_DEPTH, board.isTurnWhite()).bestMove;
+    }
+
+    private static class ScoredMove {
+        Move bestMove;
+        int score;
+
+        ScoredMove(Move move, int score) {
+            this.bestMove = move;
+            this.score = score;
+        }
+    }
+
+    private ScoredMove minimax(Board board, int depth, boolean isMaximizing) {
+        if (depth == 0) {
+            return new ScoredMove(null, evaluateBoard(board));
+        }
+
+        ArrayList<Move> possibleMoves = generateAllMoves(board, isMaximizing);
+        if (possibleMoves.isEmpty()) {
+            return new ScoredMove(null, evaluateBoard(board));
+        }
+
+        Move bestMove = null;
+        int bestScore = isMaximizing ? Integer.MIN_VALUE : Integer.MAX_VALUE;
+
+        for (Move move : possibleMoves) {
+            Board boardCopy = new Board(board); // Deep copy
+            boardCopy.playMove(move);
+
+            int score = minimax(boardCopy, depth - 1, !isMaximizing).score;
+
+            if (isMaximizing && score > bestScore) {
+                bestScore = score;
+                bestMove = move;
+            } else if (!isMaximizing && score < bestScore) {
+                bestScore = score;
+                bestMove = move;
+            }
+        }
+
+        return new ScoredMove(bestMove, bestScore);
+    }
+
+    private int evaluateBoard(Board board) {
+        int score = 0;
+        for (Piece p : board.getPieces()) {
+            int value = pieceValue(p.getType());
+            score += p.isWhite() ? value : -value;
+        }
+        return score;
+    }
+
+    private ArrayList<Move> generateAllMoves(Board board, boolean isWhite) {
         ArrayList<Move> allMoves = new ArrayList<>();
-        boolean isWhite = board.isTurnWhite();
 
         for (Piece piece : board.getPieces()) {
             if (piece.isWhite() != isWhite) continue;
-            ArrayList<int[]> legalMoves = getLegalMoves(board, piece);
+
+            ArrayList<int[]> legalMoves = board.computeLegalMoves(piece);
             for (int[] move : legalMoves) {
                 Piece target = getPieceAt(board, move[0], move[1]);
                 Move candidate = new Move(
@@ -23,26 +79,7 @@ public class AutoPlayer {
             }
         }
 
-        // Evaluate moves
+        return allMoves;
-        Move bestMove = null;
-        int bestScore = Integer.MIN_VALUE;
-
-        for (Move move : allMoves) {
-            int score = evaluateMove(move);
-            if (score > bestScore) {
-                bestScore = score;
-                bestMove = move;
-            }
-        }
-
-        return bestMove;
-    }
-
-    private int evaluateMove(Move move) {
-        if (move.getCapturedPiece() != null) {
-            return pieceValue(move.getCapturedPiece().getType());
-        }
-        return 0;
     }
 
     private int pieceValue(PieceType type) {
@@ -57,11 +94,6 @@ public class AutoPlayer {
         }
     }
 
-    private ArrayList<int[]> getLegalMoves(Board board, Piece piece) {
-        // Reuse board's internal method
-        // But we must simulate access: simulate selection, compute, and reset
-        return board.computeLegalMoves(piece);
-    }
 
     private Piece getPieceAt(Board board, int x, int y) {
         for (Piece p : board.getPieces()) {