/// <summary>
/// Iterative deepening sub-method.
/// Evaluate moves at search depth 2. Reorder. Evaluate moves at search depth 3. Reorder ...
/// </summary>
private static SingleMove IterativeDeepeningBasic(IList <SingleMove> allMoves, int searchDepth, Board board, bool isMaximizing, int timeLimitInMs = 5000)
{
//
var minimumSearchPercentForHigherDepthUse = 1 / (double)3;
var timeUp = false;
int depthUsed = 0;
var midResult = new List <(double weight, SingleMove move)>();
var currentIterationMoves = new List <SingleMove>(allMoves);
(double eval, SingleMove move)previousIterationBest = new (0.0, new SingleMove((-1, -1), (-1, -1)));
var watch = new Stopwatch();
watch.Start();
// Why this works for black start, but not white?
//var alpha = -1000000.0;
//var beta = 1000000.0;
// Initial depth 2
for (int i = 2; i <= searchDepth; i++)
{
var alpha = DefaultAlpha;
var beta = DefaultBeta;
depthUsed = i;
midResult.Clear();
foreach (var move in currentIterationMoves)
{
var newBoard = new Board(board, move);
var evaluation = MiniMax.ToDepth(newBoard, i, alpha, beta, !isMaximizing);
midResult.Add((evaluation, move));
if (isMaximizing)
{
alpha = Math.Max(alpha, evaluation);
}
else
{
beta = Math.Min(beta, evaluation);
}
if (watch.ElapsedMilliseconds > timeLimitInMs)
{
timeUp = true;
break;
}
}
// Full search finished for depth
midResult = MoveOrdering.SortWeightedMovesWithSort(midResult, isMaximizing).ToList();
// Found checkmate
//if (isMaximizing && midResult.First().weight > PieceBaseStrength.CheckMateThreshold
// || !isMaximizing && midResult.First().weight < -PieceBaseStrength.CheckMateThreshold)
//{
// // TODO This might result in stupid movements, if opponent doesn't do the exact move AI thinks is best for it
// Diagnostics.AddMessage($" Iterative deepening search depth was {depthUsed}. Check mate found.");
// Diagnostics.AddMessage($" Move evaluation: {midResult.First().weight}.");
// return midResult.First().move;
//}
if (timeUp)
{
break;
}
currentIterationMoves = midResult.Select(item => item.Item2).ToList();
previousIterationBest = midResult.First();
}
// midResult is either partial or full. Just sort and return first.
// If too small percent was searched for new depth, use prevous results
// E.g. out of 8 possible moves, only 2 were searched
if (midResult.Count / (double)allMoves.Count < minimumSearchPercentForHigherDepthUse)
{
var result = previousIterationBest;
AddIterativeDeepeningResultDiagnostics(depthUsed, allMoves.Count, midResult.Count, result.eval, result.move, board);
return(result.move);
}
var finalResult = MoveOrdering.SortWeightedMovesWithSort(midResult, isMaximizing).ToList();
AddIterativeDeepeningResultDiagnostics(depthUsed, allMoves.Count, midResult.Count, finalResult.First().weight, finalResult.First().move, board);
return(finalResult.First().move);
}
// TODO Delete. somehow unnecessary and complicated.
public static EvaluationResult GetMoveScoreList(IList <SingleMove> moves,
int searchDepth, Board board, bool isMaximizing, bool useTranspositions)
{
var result = new EvaluationResult();
var alpha = DefaultAlpha;
var beta = DefaultBeta;
if (useTranspositions)
{
foreach (var move in moves)
{
var transposition = board.Shared.Transpositions.GetTranspositionForMove(board, move);
if (transposition != null && transposition.Depth >= searchDepth)
{
// Saved some time
// TODO extra parameters to evaluationresult if this was lower or upper bound
result.Add(transposition.Evaluation, move);
}
else
{
// Board evaluation at current depth
var newBoard = new Board(board, move);
var value = MiniMax.ToDepthWithTranspositions(newBoard, searchDepth, alpha, beta,
!isMaximizing);
result.Add(value, move);
// Add new transposition table
newBoard.Shared.Transpositions.Add(newBoard.BoardHash, searchDepth, value, NodeType.Exact, newBoard.Shared.GameTurnCount);
if (isMaximizing)
{
alpha = Math.Max(alpha, value);
}
else
{
beta = Math.Min(beta, value);
}
}
}
}
else
{
foreach (var move in moves)
{
// Board evaluation at current depth
var newBoard = new Board(board, move);
var value = MiniMax.ToDepth(newBoard, searchDepth, alpha, beta, !isMaximizing);
result.Add(value, move);
if (isMaximizing)
{
alpha = Math.Max(alpha, value);
}
else
{
beta = Math.Min(beta, value);
}
}
}
return(result);
}
