static void Main(string[] args)
{
Player p1 = new Player("Red", SpaceType.Player1, PlayerColor.Red);
Player p2 = new Player("Blue", SpaceType.Player2, PlayerColor.Blue);
Game plop = new Game(p1, p2);
setUpBoard(p1, p2, plop);//calls Quinn's majestic randomized setup method that I copied over from his Eval class
plop.start();
while (true)
{
int depth = 3;//change as needed
Move p1Move = alphaBetaSearch(plop, p1, p2, depth);
plop.movePiece(p1Move.start, p1Move.end);
//update the machine learning record data on this line
if (plop.checkWin(p1))
{
//update and complete this instance of the record data with the fact that p1 won this game on this line
break;
}
Move p2Move = alphaBetaSearch(plop, p2, p1, depth);
plop.movePiece(p2Move.start, p2Move.end);
//update the machine learning record data on this line
if (plop.checkWin(p2))
{
//update and complete this instance of the record data with the fact that p2 won this game on this line
break;
}
}
}
private static double maxValue(Game state, Player max, Player min, double alpha, double beta, int depthFinal, int depthCurrent)
{
Game plop = new Game(max, min);
if (plop.checkWin(max) == true)
{
return(double.MaxValue);
}
if (plop.checkWin(min) == true)
{
return(double.MinValue);
}
//saving this section for the heuristic value to be calculated and returned once we hit max search depth
if (depthFinal == depthCurrent)
{
return(1); //heuristicValue(state,max);
}
double v = double.MinValue;
List <Move> actions = actionsForMax(state, max, min, (double)alpha, beta, depthFinal, depthCurrent);
for (int i = 0; i < actions.Count; i++)
{
if (v < actions[i].value)
{
v = actions[i].value;
}
if (v >= beta)
{
return(v);
}
if (v > alpha)
{
alpha = v;
}
}
return(v);
}
private static double minValue(Game state, Player max, Player min, double alpha, double beta, int depthFinal, int depthCurrent)
{
Game plop = new Game(min, max);
if (plop.checkWin(max) == true)
{
return(double.MaxValue);
}
if (plop.checkWin(min) == true)
{
return(double.MinValue);
}
if (depthFinal == depthCurrent)
{
return(1); //evaluation function goes here
}
double v = double.MaxValue;
List <Move> actions = actionsForMin(state, max, min, alpha, beta, depthFinal, depthCurrent);
for (int i = 0; i < actions.Count; i++)
{
if (actions[i].value < v)
{
v = actions[i].value;
}
if (v <= alpha)
{
return(v);
}
if (v < beta)
{
beta = v;
}
}
return(v);
}
