// History and stats update bonus, based on depth
int stat_bonus(Depth d) {
- return d > 14 ? 73 : 6 * d * d + 229 * d - 215;
+ return std::min((6 * d + 229) * d - 215 , 2000);
}
// Add a small random component to draw evaluations to avoid 3-fold blindness
return thisThread->state;
}
- // Skill structure is used to implement strength limit
+ // Skill structure is used to implement strength limit. If we have an uci_elo then
+ // we convert it to a suitable fractional skill level using anchoring to CCRL Elo
+ // (goldfish 1.13 = 2000) and a fit through Ordo derived Elo for match (TC 60+0.6)
+ // results spanning a wide range of k values.
struct Skill {
- explicit Skill(int l) : level(l) {}
- bool enabled() const { return level < 20; }
- bool time_to_pick(Depth depth) const { return depth == 1 + level; }
+ Skill(int skill_level, int uci_elo) {
+ if (uci_elo)
+ level = std::clamp(std::pow((uci_elo - 1346.6) / 143.4, 1 / 0.806), 0.0, 20.0);
+ else
+ level = double(skill_level);
+ }
+ bool enabled() const { return level < 20.0; }
+ bool time_to_pick(Depth depth) const { return depth == 1 + int(level); }
Move pick_best(size_t multiPV);
- int level;
+ double level;
Move best = MOVE_NONE;
};
Time.availableNodes += Limits.inc[us] - Threads.nodes_searched();
Thread* bestThread = this;
+ Skill skill = Skill(Options["Skill Level"], Options["UCI_LimitStrength"] ? int(Options["UCI_Elo"]) : 0);
if ( int(Options["MultiPV"]) == 1
&& !Limits.depth
- && !(Skill(Options["Skill Level"]).enabled() || int(Options["UCI_LimitStrength"]))
+ && !skill.enabled()
&& rootMoves[0].pv[0] != MOVE_NONE)
bestThread = Threads.get_best_thread();
// The latter is needed for statScore and killer initialization.
Stack stack[MAX_PLY+10], *ss = stack+7;
Move pv[MAX_PLY+1];
- Value bestValue, alpha, beta, delta;
+ Value alpha, beta, delta;
Move lastBestMove = MOVE_NONE;
Depth lastBestMoveDepth = 0;
MainThread* mainThread = (this == Threads.main() ? Threads.main() : nullptr);
std::fill(&lowPlyHistory[MAX_LPH - 2][0], &lowPlyHistory.back().back() + 1, 0);
size_t multiPV = size_t(Options["MultiPV"]);
-
- // Pick integer skill levels, but non-deterministically round up or down
- // such that the average integer skill corresponds to the input floating point one.
- // UCI_Elo is converted to a suitable fractional skill level, using anchoring
- // to CCRL Elo (goldfish 1.13 = 2000) and a fit through Ordo derived Elo
- // for match (TC 60+0.6) results spanning a wide range of k values.
- PRNG rng(now());
- double floatLevel = Options["UCI_LimitStrength"] ?
- std::clamp(std::pow((Options["UCI_Elo"] - 1346.6) / 143.4, 1 / 0.806), 0.0, 20.0) :
- double(Options["Skill Level"]);
- int intLevel = int(floatLevel) +
- ((floatLevel - int(floatLevel)) * 1024 > rng.rand<unsigned>() % 1024 ? 1 : 0);
- Skill skill(intLevel);
+ Skill skill(Options["Skill Level"], Options["UCI_LimitStrength"] ? int(Options["UCI_Elo"]) : 0);
// When playing with strength handicap enable MultiPV search that we will
// use behind the scenes to retrieve a set of possible moves.
multiPV = std::min(multiPV, rootMoves.size());
- ttHitAverage.set(50, 100); // initialize the running average at 50%
doubleExtensionAverage[WHITE].set(0, 100); // initialize the running average at 0%
doubleExtensionAverage[BLACK].set(0, 100); // initialize the running average at 0%
nodesLastExplosive = nodes;
nodesLastNormal = nodes;
- state = EXPLOSION_NONE;
- trend = SCORE_ZERO;
+ state = EXPLOSION_NONE;
+ trend = SCORE_ZERO;
+ optimism[ us] = Value(25);
+ optimism[~us] = -optimism[us];
int searchAgainCounter = 0;
// Reset aspiration window starting size
if (rootDepth >= 4)
{
- Value prev = rootMoves[pvIdx].previousScore;
- delta = Value(17);
+ Value prev = rootMoves[pvIdx].averageScore;
+ delta = Value(17) + int(prev) * prev / 16384;
alpha = std::max(prev - delta,-VALUE_INFINITE);
beta = std::min(prev + delta, VALUE_INFINITE);
- // Adjust trend based on root move's previousScore (dynamic contempt)
- int tr = 113 * prev / (abs(prev) + 147);
-
+ // Adjust trend and optimism based on root move's previousScore
+ int tr = sigmoid(prev, 0, 0, 147, 113, 1);
trend = (us == WHITE ? make_score(tr, tr / 2)
: -make_score(tr, tr / 2));
+
+ int opt = sigmoid(prev, 0, 25, 147, 14464, 256);
+ optimism[ us] = Value(opt);
+ optimism[~us] = -optimism[us];
}
// Start with a small aspiration window and, in the case of a fail
Value bestValue, value, ttValue, eval, maxValue, probCutBeta;
bool givesCheck, improving, didLMR, priorCapture;
bool captureOrPromotion, doFullDepthSearch, moveCountPruning,
- ttCapture, singularQuietLMR, noLMRExtension;
+ ttCapture, singularQuietLMR;
Piece movedPiece;
- int moveCount, captureCount, quietCount;
+ int moveCount, captureCount, quietCount, bestMoveCount, improvement;
// Step 1. Initialize node
ss->inCheck = pos.checkers();
priorCapture = pos.captured_piece();
Color us = pos.side_to_move();
- moveCount = captureCount = quietCount = ss->moveCount = 0;
+ moveCount = bestMoveCount = captureCount = quietCount = ss->moveCount = 0;
bestValue = -VALUE_INFINITE;
maxValue = VALUE_INFINITE;
if (alpha >= beta)
return alpha;
}
+ else
+ thisThread->rootDelta = beta - alpha;
assert(0 <= ss->ply && ss->ply < MAX_PLY);
ttValue = ss->ttHit ? value_from_tt(tte->value(), ss->ply, pos.rule50_count()) : VALUE_NONE;
ttMove = rootNode ? thisThread->rootMoves[thisThread->pvIdx].pv[0]
: ss->ttHit ? tte->move() : MOVE_NONE;
+ ttCapture = ttMove && pos.capture_or_promotion(ttMove);
if (!excludedMove)
ss->ttPv = PvNode || (ss->ttHit && tte->is_pv());
&& is_ok((ss-1)->currentMove))
thisThread->lowPlyHistory[ss->ply - 1][from_to((ss-1)->currentMove)] << stat_bonus(depth - 5);
- // running average of ttHit
- thisThread->ttHitAverage.update(ss->ttHit);
-
// At non-PV nodes we check for an early TT cutoff
if ( !PvNode
&& ss->ttHit
- && tte->depth() >= depth
+ && tte->depth() > depth - (thisThread->id() % 2 == 1)
&& ttValue != VALUE_NONE // Possible in case of TT access race
&& (ttValue >= beta ? (tte->bound() & BOUND_LOWER)
: (tte->bound() & BOUND_UPPER)))
if (ttValue >= beta)
{
// Bonus for a quiet ttMove that fails high
- if (!pos.capture_or_promotion(ttMove))
+ if (!ttCapture)
update_quiet_stats(pos, ss, ttMove, stat_bonus(depth), depth);
// Extra penalty for early quiet moves of the previous ply
update_continuation_histories(ss-1, pos.piece_on(prevSq), prevSq, -stat_bonus(depth + 1));
}
// Penalty for a quiet ttMove that fails low
- else if (!pos.capture_or_promotion(ttMove))
+ else if (!ttCapture)
{
int penalty = -stat_bonus(depth);
thisThread->mainHistory[us][from_to(ttMove)] << penalty;
// Skip early pruning when in check
ss->staticEval = eval = VALUE_NONE;
improving = false;
+ improvement = 0;
goto moves_loop;
}
else if (ss->ttHit)
}
else
{
- // In case of null move search use previous static eval with a different sign
- // and addition of two tempos
- if ((ss-1)->currentMove != MOVE_NULL)
- ss->staticEval = eval = evaluate(pos);
- else
- ss->staticEval = eval = -(ss-1)->staticEval;
+ ss->staticEval = eval = evaluate(pos);
// Save static evaluation into transposition table
- if(!excludedMove)
- tte->save(posKey, VALUE_NONE, ss->ttPv, BOUND_NONE, DEPTH_NONE, MOVE_NONE, eval);
+ if (!excludedMove)
+ tte->save(posKey, VALUE_NONE, ss->ttPv, BOUND_NONE, DEPTH_NONE, MOVE_NONE, eval);
}
// Use static evaluation difference to improve quiet move ordering
if (is_ok((ss-1)->currentMove) && !(ss-1)->inCheck && !priorCapture)
{
- int bonus = std::clamp(-depth * 4 * int((ss-1)->staticEval + ss->staticEval), -1000, 1000);
+ int bonus = std::clamp(-16 * int((ss-1)->staticEval + ss->staticEval), -2000, 2000);
thisThread->mainHistory[~us][from_to((ss-1)->currentMove)] << bonus;
}
- // Set up improving flag that is used in various pruning heuristics
- // We define position as improving if static evaluation of position is better
- // Than the previous static evaluation at our turn
- // In case of us being in check at our previous move we look at move prior to it
- improving = (ss-2)->staticEval == VALUE_NONE
- ? ss->staticEval > (ss-4)->staticEval || (ss-4)->staticEval == VALUE_NONE
- : ss->staticEval > (ss-2)->staticEval;
+ // Set up the improvement variable, which is the difference between the current
+ // static evaluation and the previous static evaluation at our turn (if we were
+ // in check at our previous move we look at the move prior to it). The improvement
+ // margin and the improving flag are used in various pruning heuristics.
+ improvement = (ss-2)->staticEval != VALUE_NONE ? ss->staticEval - (ss-2)->staticEval
+ : (ss-4)->staticEval != VALUE_NONE ? ss->staticEval - (ss-4)->staticEval
+ : 200;
+
+ improving = improvement > 0;
// Step 7. Futility pruning: child node (~50 Elo).
// The depth condition is important for mate finding.
- if ( !PvNode
+ if ( !ss->ttPv
&& depth < 9
&& eval - futility_margin(depth, improving) >= beta
- && eval < VALUE_KNOWN_WIN) // Do not return unproven wins
+ && eval < 15000) // 50% larger than VALUE_KNOWN_WIN, but smaller than TB wins.
return eval;
// Step 8. Null move search with verification search (~40 Elo)
&& (ss-1)->statScore < 23767
&& eval >= beta
&& eval >= ss->staticEval
- && ss->staticEval >= beta - 20 * depth - 22 * improving + 168 * ss->ttPv + 177
+ && ss->staticEval >= beta - 20 * depth - improvement / 15 + 204
&& !excludedMove
&& pos.non_pawn_material(us)
&& (ss->ply >= thisThread->nmpMinPly || us != thisThread->nmpColor))
assert(probCutBeta < VALUE_INFINITE);
MovePicker mp(pos, ttMove, probCutBeta - ss->staticEval, &captureHistory);
- int probCutCount = 0;
bool ttPv = ss->ttPv;
ss->ttPv = false;
- while ( (move = mp.next_move()) != MOVE_NONE
- && probCutCount < 2 + 2 * cutNode)
+ while ((move = mp.next_move()) != MOVE_NONE)
if (move != excludedMove && pos.legal(move))
{
assert(pos.capture_or_promotion(move));
assert(depth >= 5);
captureOrPromotion = true;
- probCutCount++;
ss->currentMove = move;
ss->continuationHistory = &thisThread->continuationHistory[ss->inCheck]
moves_loop: // When in check, search starts here
- ttCapture = ttMove && pos.capture_or_promotion(ttMove);
int rangeReduction = 0;
// Step 11. A small Probcut idea, when we are in check
ss->ply);
value = bestValue;
- singularQuietLMR = moveCountPruning = noLMRExtension = false;
+ singularQuietLMR = moveCountPruning = false;
// Indicate PvNodes that will probably fail low if the node was searched
// at a depth equal or greater than the current depth, and the result of this search was a fail low.
}
else
{
+ int history = (*contHist[0])[movedPiece][to_sq(move)]
+ + (*contHist[1])[movedPiece][to_sq(move)]
+ + (*contHist[3])[movedPiece][to_sq(move)];
+
// Continuation history based pruning (~20 Elo)
- if (lmrDepth < 5
- && (*contHist[0])[movedPiece][to_sq(move)]
- + (*contHist[1])[movedPiece][to_sq(move)]
- + (*contHist[3])[movedPiece][to_sq(move)] < -3000 * depth + 3000)
+ if ( lmrDepth < 5
+ && history < -3000 * depth + 3000)
continue;
+ history += thisThread->mainHistory[us][from_to(move)];
+
// Futility pruning: parent node (~5 Elo)
if ( !ss->inCheck
&& lmrDepth < 8
- && ss->staticEval + 172 + 145 * lmrDepth <= alpha)
+ && ss->staticEval + 142 + 139 * lmrDepth + history / 64 <= alpha)
continue;
// Prune moves with negative SEE (~20 Elo)
if ( !PvNode
&& value < singularBeta - 75
&& ss->doubleExtensions <= 6)
- {
extension = 2;
- noLMRExtension = true;
- }
}
// Multi-cut pruning
else if (singularBeta >= beta)
return singularBeta;
- // If the eval of ttMove is greater than beta we try also if there is another
- // move that pushes it over beta, if so the position also has probably multiple
- // moves giving fail highs. We will then reduce the ttMove (negative extension).
+ // If the eval of ttMove is greater than beta, we reduce it (negative extension)
else if (ttValue >= beta)
- {
- ss->excludedMove = move;
- value = search<NonPV>(pos, ss, beta - 1, beta, (depth + 3) / 2, cutNode);
- ss->excludedMove = MOVE_NONE;
-
- if (value >= beta)
- extension = -2;
- }
+ extension = -2;
}
// Capture extensions for PvNodes and cutNodes
// cases where we extend a son if it has good chances to be "interesting".
if ( depth >= 3
&& moveCount > 1 + 2 * rootNode
- && ( !captureOrPromotion
- || (cutNode && (ss-1)->moveCount > 1)
- || !ss->ttPv)
- && (!PvNode || ss->ply > 1 || thisThread->id() % 4 != 3))
+ && ( !ss->ttPv
+ || !captureOrPromotion
+ || (cutNode && (ss-1)->moveCount > 1)))
{
Depth r = reduction(improving, depth, moveCount, rangeReduction > 2);
- if (PvNode)
- r--;
-
- // Decrease reduction if the ttHit running average is large (~0 Elo)
- if (thisThread->ttHitAverage.is_greater(537, 1024))
+ // Decrease reduction at some PvNodes (~2 Elo)
+ if ( PvNode
+ && bestMoveCount <= 3
+ && beta - alpha >= thisThread->rootDelta / 4)
r--;
// Decrease reduction if position is or has been on the PV
// In general we want to cap the LMR depth search at newDepth. But if reductions
// are really negative and movecount is low, we allow this move to be searched
- // deeper than the first move (this may lead to hidden double extensions if
- // newDepth got its own extension before).
- int deeper = r >= -1 ? 0
- : noLMRExtension ? 0
- : moveCount <= 5 ? 1
- : (depth > 6 && PvNode) ? 1
- : 0;
+ // deeper than the first move (this may lead to hidden double extensions).
+ int deeper = r >= -1 ? 0
+ : moveCount <= 5 ? 2
+ : PvNode && depth > 6 ? 1
+ : cutNode && moveCount <= 7 ? 1
+ : 0;
Depth d = std::clamp(newDepth - r, 1, newDepth + deeper);
RootMove& rm = *std::find(thisThread->rootMoves.begin(),
thisThread->rootMoves.end(), move);
+ rm.averageScore = rm.averageScore != -VALUE_INFINITE ? (2 * value + rm.averageScore) / 3 : value;
+
// PV move or new best move?
if (moveCount == 1 || value > alpha)
{
for (Move* m = (ss+1)->pv; *m != MOVE_NONE; ++m)
rm.pv.push_back(*m);
- // We record how often the best move has been changed in each
- // iteration. This information is used for time management and LMR
- if (moveCount > 1)
+ // We record how often the best move has been changed in each iteration.
+ // This information is used for time management and LMR. In MultiPV mode,
+ // we must take care to only do this for the first PV line.
+ if ( moveCount > 1
+ && !thisThread->pvIdx)
++thisThread->bestMoveChanges;
}
else
update_pv(ss->pv, move, (ss+1)->pv);
if (PvNode && value < beta) // Update alpha! Always alpha < beta
+ {
alpha = value;
+ bestMoveCount++;
+ }
else
{
assert(value >= beta); // Fail high
Key posKey;
Move ttMove, move, bestMove;
Depth ttDepth;
- Value bestValue, value, ttValue, futilityValue, futilityBase, oldAlpha;
+ Value bestValue, value, ttValue, futilityValue, futilityBase;
bool pvHit, givesCheck, captureOrPromotion;
int moveCount;
if (PvNode)
{
- oldAlpha = alpha; // To flag BOUND_EXACT when eval above alpha and no available moves
(ss+1)->pv = pv;
ss->pv[0] = MOVE_NONE;
}
}
else
// In case of null move search use previous static eval with a different sign
- // and addition of two tempos
ss->staticEval = bestValue =
(ss-1)->currentMove != MOVE_NULL ? evaluate(pos)
: -(ss-1)->staticEval;
// Save gathered info in transposition table
tte->save(posKey, value_to_tt(bestValue, ss->ply), pvHit,
- bestValue >= beta ? BOUND_LOWER :
- PvNode && bestValue > oldAlpha ? BOUND_EXACT : BOUND_UPPER,
+ bestValue >= beta ? BOUND_LOWER : BOUND_UPPER,
ttDepth, bestMove, ss->staticEval);
assert(bestValue > -VALUE_INFINITE && bestValue < VALUE_INFINITE);
PieceType captured = type_of(pos.piece_on(to_sq(bestMove)));
bonus1 = stat_bonus(depth + 1);
- bonus2 = bestValue > beta + PawnValueMg ? bonus1 // larger bonus
- : std::min(bonus1, stat_bonus(depth)); // smaller bonus
+ bonus2 = bestValue > beta + PawnValueMg ? bonus1 // larger bonus
+ : stat_bonus(depth); // smaller bonus
if (!pos.capture_or_promotion(bestMove))
{
thisThread->mainHistory[us][from_to(move)] << bonus;
update_continuation_histories(ss, pos.moved_piece(move), to_sq(move), bonus);
- // Penalty for reversed move in case of moved piece not being a pawn
- if (type_of(pos.moved_piece(move)) != PAWN)
- thisThread->mainHistory[us][from_to(reverse_move(move))] << -bonus;
-
// Update countermove history
if (is_ok((ss-1)->currentMove))
{
// RootMoves are already sorted by score in descending order
Value topScore = rootMoves[0].score;
int delta = std::min(topScore - rootMoves[multiPV - 1].score, PawnValueMg);
- int weakness = 120 - 2 * level;
int maxScore = -VALUE_INFINITE;
+ double weakness = 120 - 2 * level;
// Choose best move. For each move score we add two terms, both dependent on
// weakness. One is deterministic and bigger for weaker levels, and one is
for (size_t i = 0; i < multiPV; ++i)
{
// This is our magic formula
- int push = ( weakness * int(topScore - rootMoves[i].score)
- + delta * (rng.rand<unsigned>() % weakness)) / 128;
+ int push = int(( weakness * int(topScore - rootMoves[i].score)
+ + delta * (rng.rand<unsigned>() % int(weakness))) / 128);
if (rootMoves[i].score + push >= maxScore)
{
projects / stockfish / blobdiff