public (int, int) GetNextMove(Board.Board board)
{
DateTime startTime = DateTime.Now;
LinkedList <(int, int)> moves = board.GetLegalMoves();
(int, int)selectedMove = (-1, -1);
int score = int.MinValue / 2;
int value = score;
int alpha = int.MinValue;
int beta = int.MaxValue;
// treat no valid move options
if (moves.Count == 0)
{
}
foreach ((int, int)move in moves)
{
board.Move(move);
if (board.remainingActions <= 1)
{
value = AB(ref board, Math.Max(alpha, score), beta, depth - 1);
}
else
{
value = -AB(ref board, -1 * beta, -1 * Math.Max(alpha, score), depth - 1);
}
if (value > score)
{
score = value;
selectedMove = move;
}
board.Undo();
if (score > beta)
{
break;
}
}
DateTime endTime = DateTime.Now;
var timeDelta = endTime - startTime;
timer = timer - (int)timeDelta.TotalSeconds;
Console.WriteLine($"{timer} seconds remaining");
if (timer < 180)
{
depth = 3;
}
else if (timer < 45)
{
depth = 2;
}
return(selectedMove);
}
public (int, int) GetNextMove(Board.Board board)
{
Random random = new Random();
var legalMoves = board.GetLegalMoves();
int randomIndex = random.Next(legalMoves.Count);
return(legalMoves.ToArray()[randomIndex]);
throw new NotImplementedException();
}
private int AB(ref Board.Board board, int alpha, int beta, int depth) // maybe can implement minimal search window and deep pruning by giving alpha and beta as reference
{
if (depth == 0 || board.CheckTerminalPosition() >= 0)
{
return(evaluationHeuristic.Evaluate(board, board.activePlayer) + random.Next(-randomRange, randomRange));
}
LinkedList <(int, int)> moves = board.GetLegalMoves();
if (moves.Count == 0)
{
return(evaluationHeuristic.Evaluate(board, board.activePlayer) + random.Next(-randomRange, randomRange));
}
int score = int.MinValue / 2;
int value = score;
foreach ((int, int)move in moves)
{
board.Move(move);
if (board.remainingActions <= 1)
{
value = AB(ref board, Math.Max(alpha, score), beta, depth - 1);
}
else
{
value = -AB(ref board, -1 * beta, -1 * Math.Max(alpha, score), depth - 1);
}
if (value > score)
{
score = value;
}
board.Undo();
if (score > beta)
{
break;
}
}
return(score);
}
public (int, int) GetNextMove(Board.Board board)
{
LinkedList <(int, int)> moves = board.GetLegalMoves();
(int, int)selectedMove = (-1, -1);
int score = int.MinValue / 2;
int value = score;
int alpha = int.MinValue;
int beta = int.MaxValue;
foreach ((int, int)move in moves)
{
board.Move(move);
if (board.remainingActions <= 1)
{
value = AB(ref board, Math.Max(alpha, score), beta, depth - 1);
}
else
{
value = -AB(ref board, -1 * beta, -1 * Math.Max(alpha, score), depth - 1);
}
if (value > score)
{
score = value;
selectedMove = move;
}
board.Undo();
if (score > beta)
{
break;
}
}
return(selectedMove);
}
private int AB(ref Board.Board board, int alpha, int beta, int depth) // maybe can implement minimal search window and deep pruning by giving alpha and beta as reference
{
if (depth == 0)
{
//selective deepening in case of captures, might want to extend to other more or less forcing moves ("check", looming escpae)
// right now seems to slow down the calculations considerably, if tt doesnt help, might consider also to simplify it to only search recapturing sequences
if (board.captures.Count > 0 && board.captures.Last.Value.Item1 == (board.turnCounter - 1))
{
int selectScore = int.MinValue / 2;
int selectValue = selectScore;
LinkedList <(int, int)> forcingMoves = board.GetForcingMoves();
foreach ((int, int)move in forcingMoves)
{
board.Move(move);
if (board.remainingActions <= 1)
{
selectValue = AB(ref board, Math.Max(alpha, selectScore), beta, 0);
}
else
{
selectValue = -AB(ref board, -1 * beta, -1 * Math.Max(alpha, selectScore), 0);
}
if (selectValue > selectScore)
{
selectScore = selectValue;
}
board.Undo();
if (selectScore > beta)
{
break;
}
}
return(selectScore);
}
return(evaluationHeuristic.Evaluate(board, board.activePlayer) + random.Next(-randomRange, randomRange));
}
if (depth == 0 || board.CheckTerminalPosition() >= 0)
{
return(evaluationHeuristic.Evaluate(board, board.activePlayer) + random.Next(-randomRange, randomRange));
}
LinkedList <(int, int)> moves = board.GetLegalMoves();
if (moves.Count == 0)
{
return(evaluationHeuristic.Evaluate(board, board.activePlayer) + random.Next(-randomRange, randomRange));
}
int score = int.MinValue / 2;
int value = score;
foreach ((int, int)move in moves)
{
board.Move(move);
if (board.remainingActions <= 1)
{
value = AB(ref board, Math.Max(alpha, score), beta, depth - 1);
}
else
{
value = -AB(ref board, -1 * beta, -1 * Math.Max(alpha, score), depth - 1);
}
if (value > score)
{
score = value;
}
board.Undo();
if (score > beta)
{
break;
}
}
return(score);
}