// The minimax algorithm itself
private ScoreMovePair minimax(Dictionary <int, PieceState> board, int turn, int maxDepth, int currDepth, int prune, int moveCount)
{
// Checks if either at end of game state or as far down in the tree we are suppose to be. If so uses the
// static evaluation method appropriate for diffulty settings
if (isGameOver(board) || currDepth == maxDepth)
{
if (GameOptions.Instance.SMART)
{
if (currDepth % 2 == 0)
{
if (moveCount >= SWITCH_TIME) // If at the end of game try to maximize the number of pieces instead of playing position
{
return(new ScoreMovePair(-1, unweightedEvaluation(board, (turn))));
}
else
{
return(new ScoreMovePair(-1, weightedEvaluation(board, (turn))));
}
}
else
{
if (moveCount >= SWITCH_TIME) // If at the end of game try to maximize the number of pieces instead of playing position
{
return(new ScoreMovePair(-1, unweightedEvaluation(board, (turn == 0) ? 1 : 0)));
}
else
{
return(new ScoreMovePair(-1, weightedEvaluation(board, (turn == 0) ? 1 : 0))); // uses positional strategy
}
}
}
else
{
if (maxDepth % 2 == 0)
{
return(new ScoreMovePair(-1, unweightedEvaluation(board, (turn))));
}
else
{
return(new ScoreMovePair(-1, unweightedEvaluation(board, (turn == 0) ? 1 : 0)));
}
}
}
ScoreMovePair best = new ScoreMovePair(-1, 0); // Initialize best move to none
if (turn == 0) // Turn is AI's
{
best.Score = int.MinValue;
}
else // Turn is player's
{
best.Score = int.MaxValue;
}
for (int i = 0; i < 60; i++) // Iterate through board positions
{
int id = (turn == 0) ? ordered[i] : reverseOrdered[i];
// If move not available skip
if (board[id] != PieceState.NONE || !isViable(board, id, turn))
{
continue;
}
// Create copy of board for new board
Dictionary <int, PieceState> newBoard = new Dictionary <int, PieceState>();
foreach (KeyValuePair <int, PieceState> b in board)
{
newBoard.Add(b.Key, b.Value);
}
// Attempt a move and evaluate it
if (MakeMove(newBoard, id, turn, false) == 0)
{
continue;
}
newBoard[id] = (turn == 0) ? PieceState.BLACK : PieceState.WHITE;
ScoreMovePair current = minimax(newBoard, (turn == 0) ? 1 : 0, maxDepth, currDepth + 1, best.Score, moveCount + 1);
if (turn == 0) // Maximize
{
if (current.Score > prune)
{
return(current);
}
if (current.Score > best.Score)
{
best.Score = current.Score;
best.Move = id;
}
}
else // Minimize
{
if (current.Score < prune)
{
return(current);
}
if (current.Score < best.Score)
{
best.Score = current.Score;
best.Move = id;
}
}
}
return(best);
}
private ScoreMovePair minimax(Dictionary <int, PieceState> board, int turn, int maxDepth, int currDepth, int prune)
{
if (isGameOver(board) || currDepth == maxDepth)
{
if (GameOptions.Instance.SMART)
{
return(new ScoreMovePair(-1, weightedEvaluation(board, (turn == 0) ? 1 : 0)));
}
else
{
return(new ScoreMovePair(-1, unweightedEvaluation(board, (turn == 0) ? 1 : 0)));
}
}
ScoreMovePair best = new ScoreMovePair(-1, 0); // Initialize best move to none
if (turn == 0) // Turn is AI's
{
best.Score = int.MinValue;
}
else // Turn is player's
{
best.Score = int.MaxValue;
}
for (int i = 0; i < 64; i++) // Iterate through board positions
{
// If move not available skip
if (board[i] != PieceState.NONE)
{
continue;
}
// Create copy of board for new board
Dictionary <int, PieceState> newBoard = new Dictionary <int, PieceState>();
foreach (KeyValuePair <int, PieceState> b in board)
{
newBoard.Add(b.Key, b.Value);
}
// Attempt a move and evaluate it
if (MakeMove(newBoard, i, turn, false) == 0)
{
continue;
}
newBoard[i] = (turn == 0) ? PieceState.BLACK : PieceState.WHITE;
ScoreMovePair current = minimax(newBoard, (turn == 0) ? 1 : 0, maxDepth, currDepth + 1, best.Score);
if (turn == 0) // Maximize
{
if (current.Score > prune)
{
return(current);
}
if (current.Score > best.Score)
{
best.Score = current.Score;
best.Move = i;
}
}
else // Minimize
{
if (current.Score < prune)
{
return(current);
}
if (current.Score < best.Score)
{
best.Score = current.Score;
best.Move = i;
}
}
}
return(best);
}
