/// <summary>
/// The main game loop. plays while both kings are still standing.
/// </summary>
public void GameLoop()
{
DisplayGameBoard();
SmartAgent myAgent = new SmartAgent();
while (FindKings())
{
if (currentTurn)
{
// List<PotentialMove> chosenMoveList = myAgent.MiniMaxDecision(board, 6, currentTurn, null, null, -999, 999);
// PotentialMove chosenMove = chosenMoveList[chosenMoveList.Count - 2];
// ValidateFieldAndPiece(chosenMove.PreviousPosition[0], chosenMove.PreviousPosition[1], chosenMove.NewPosition[0], chosenMove.NewPosition[1]);
PlayTurn();
DisplayGameBoard();
}
else
{
List <PotentialMove> chosenMoveList = myAgent.MiniMaxDecision(board, 6, currentTurn, null, null, -999, 999);
PotentialMove chosenMove = chosenMoveList[chosenMoveList.Count - 2];
ValidateFieldAndPiece(chosenMove.PreviousPosition[0], chosenMove.PreviousPosition[1], chosenMove.NewPosition[0], chosenMove.NewPosition[1]);
DisplayGameBoard();
Console.WriteLine("Player " + currentTurn + " brought piece from field " + chosenMove.PreviousPosition[0] + "," + chosenMove.PreviousPosition[1] + " to field " + chosenMove.NewPosition[0] + "," + chosenMove.NewPosition[1]);
}
currentTurn = !currentTurn;
}
DisplayGameBoard();
}
/// <summary>
/// The recursive function to pick a move using the minimax algorithm
/// </summary>
/// <param name="currentState">The current gameboard's state</param>
/// <param name="depth">the depth of the recursive calls</param>
/// <param name="currentPlayer">The player currently playing</param>
/// <param name="previousPosition">The previous position of the piece that moved in the last round</param>
/// <param name="newPosition">The new position of the piece that moved in the last round</param>
/// <param name="bestAlpha">The best move alpha could make. Used for pruning</param>
/// <param name="bestBeta">The best move beta could make. Used for pruning</param>
/// <returns>the list of optimal moves</returns>
public List <PotentialMove> MiniMaxDecision(Piece[,] currentState, int depth, bool currentPlayer, int[] previousPosition, int[] newPosition, int bestAlpha, int bestBeta)
{
List <PotentialMove> possibleActions, evalList;
List <PotentialMove> bestAction = new List <PotentialMove>();
List <int> possibleActionsEvaluation = new List <int>();
if (depth == 0)
{
PotentialMove move = new PotentialMove(previousPosition, newPosition, currentState);
List <PotentialMove> returnList = new List <PotentialMove>();
returnList.Add(move);
return(returnList);
}
else if (currentPlayer)
{
int maxEval = -999;
possibleActions = ListAllPossibleActions(currentState, currentPlayer);
foreach (var v in possibleActions)
{
evalList = MiniMaxDecision(v.CurrentState, depth - 1, !currentPlayer, v.PreviousPosition, v.NewPosition, bestAlpha, bestBeta);
PotentialMove eval = evalList[evalList.Count - 1];
if (CountUtility(eval.CurrentState) > maxEval)
{
evalList.Add(new PotentialMove(previousPosition, newPosition, currentState));
bestAction = evalList;
maxEval = CountUtility(eval.CurrentState);
}
if (bestAlpha < CountUtility(eval.CurrentState))
{
bestAlpha = CountUtility(eval.CurrentState);
}
if (bestBeta <= bestAlpha)
{
break;
}
}
return(bestAction);
}
else
{
int minEval = 999;
possibleActions = ListAllPossibleActions(currentState, currentPlayer);
foreach (var v in possibleActions)
{
evalList = MiniMaxDecision(v.CurrentState, depth - 1, !currentPlayer, v.PreviousPosition, v.NewPosition, bestAlpha, bestBeta);
PotentialMove eval = evalList[evalList.Count - 1];
if (CountUtility(eval.CurrentState) < minEval)
{
evalList.Add(new PotentialMove(previousPosition, newPosition, currentState));
bestAction = evalList;
minEval = CountUtility(eval.CurrentState);
}
if (bestBeta > CountUtility(eval.CurrentState))
{
bestBeta = CountUtility(eval.CurrentState);
}
if (bestBeta <= bestAlpha)
{
break;
}
}
return(bestAction);
}
}
