/// <summary>
/// Acts similarly to normal alpha beta search but instead of generating new board nodes, traverses existing board node list.
/// </summary>
/// <param name="_node"></param>
/// <param name="_depth"></param>
/// <param name="_alpha"></param>
/// <param name="_beta"></param>
/// <param name="_searchingPlayer"></param>
/// <param name="_maximizingPlayer"></param>
/// <param name="_player"></param>
/// <param name="_accuracyMod"></param>
/// <returns></returns>
static public float TraverseNodeList(BoardNode _node, int _depth, float _alpha, float _beta, int _searchingPlayer, bool _maximizingPlayer, AI _player, float _accuracyMod)
{
float v;
if (_depth == 0 || _node.MoveCount() == 0)
{
if (_maximizingPlayer)
{
v = (float)_node.GetValue(_player);
if (_accuracyMod != 0)
{
System.Random random = new MathNet.Numerics.Random.SystemRandomSource();
v = v + (float)random.NextDouble() * (2 * _accuracyMod) - _accuracyMod;
}
return(v);
}
else
{
return(AlphaBeta(_node, _depth, _alpha, _beta, _searchingPlayer, _maximizingPlayer, _player, _accuracyMod));
}
}
if (!_node.IsEndNode())
{
if (_maximizingPlayer)
{
v = -Mathf.Infinity;
foreach (BoardNode b in _node.GetChildren())
{
v = Mathf.Max(v, TraverseNodeList(b, _depth - 1, _alpha, _beta, _searchingPlayer, false, _player, _accuracyMod));
_alpha = Mathf.Max(_alpha, v);
if (_beta <= _alpha)
{
break;
}
}
return(v);
}
else
{
v = 1;
foreach (BoardNode b in _node.GetChildren())
{
v = Mathf.Min(v, TraverseNodeList(b, _depth - 1, _alpha, _beta, _searchingPlayer, true, _player, _accuracyMod));
_beta = Mathf.Min(_beta, v);
if (_beta <= _alpha)
{
break;
}
}
return(v);
}
}
else
{
return(AlphaBeta(_node, _depth, _alpha, _beta, _searchingPlayer, _maximizingPlayer, _player, _accuracyMod));
}
}
static public float AlphaBeta(BoardNode _node, int _depth, float _alpha, float _beta, int _searchingPlayer, bool _maximizingPlayer, AI _player, float _accuracyMod)
{
float v;
//If we have reach the end of the search
if (_depth == 0 || _node.MoveCount() == 0)
{
//If we are currently on the maximising player then find the board rating and return
if (_maximizingPlayer)
{
v = (float)_node.GetValue(_player);
if (_accuracyMod != 0)
{
System.Random random = new MathNet.Numerics.Random.SystemRandomSource();
v = v + (float)random.NextDouble() * (2 * _accuracyMod) - _accuracyMod;
}
return(v);
}
//If we are not currently on the maximising player then jump one extra layer.
else
{
v = 1;
foreach (StoneMove m in _node.GetMoveList())
{
Board testBoard = _node.boardState.Clone();
testBoard.ResolveMove(m);
_node.AddChild(new BoardNode(testBoard, _searchingPlayer, m));
}
foreach (BoardNode b in _node.GetChildren())
{
v = Mathf.Min(v, AlphaBeta(b, _depth - 1, _alpha, _beta, _searchingPlayer, true, _player, _accuracyMod));
_beta = Mathf.Min(_beta, v);
if (_beta <= _alpha)
{
break;
}
}
return(v);
}
}
if (_maximizingPlayer)
{
v = -Mathf.Infinity;
foreach (StoneMove m in _node.GetMoveList())
{
Board testBoard = _node.boardState.Clone();
testBoard.ResolveMove(m);
_node.AddChild(new BoardNode(testBoard, 3 - _searchingPlayer, m));
}
foreach (BoardNode b in _node.GetChildren())
{
v = Mathf.Max(v, AlphaBeta(b, _depth - 1, _alpha, _beta, _searchingPlayer, false, _player, _accuracyMod));
_alpha = Mathf.Max(_alpha, v);
if (_beta <= _alpha)
{
break;
}
}
return(v);
}
else
{
v = +Mathf.Infinity;
foreach (StoneMove m in _node.GetMoveList())
{
Board testBoard = _node.boardState.Clone();
testBoard.ResolveMove(m);
_node.AddChild(new BoardNode(testBoard, _searchingPlayer, m));
}
foreach (BoardNode b in _node.GetChildren())
{
v = Mathf.Min(v, AlphaBeta(b, _depth - 1, _alpha, _beta, _searchingPlayer, true, _player, _accuracyMod));
_beta = Mathf.Min(_beta, v);
if (_beta <= _alpha)
{
break;
}
}
return(v);
}
}