X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fsearch.cpp;h=c57a13293a68bcbff78ec3631361e8fae0f410e1;hb=f7ef48b4780e86d794d9bccbb2d0e2b8d346fa32;hp=0b069e2f581e804589289d2fb18251c63cc7f0a7;hpb=8d4caebabe91a473bd052d2f771e79a184902c31;p=stockfish diff --git a/src/search.cpp b/src/search.cpp index 0b069e2f..c57a1329 100644 --- a/src/search.cpp +++ b/src/search.cpp @@ -257,6 +257,7 @@ namespace { // Skill level adjustment int SkillLevel; + bool SkillLevelEnabled; RKISS RK; // Multi-threads manager object @@ -293,7 +294,6 @@ namespace { bool check_is_dangerous(Position &pos, Move move, Value futilityBase, Value beta, Value *bValue); bool connected_moves(const Position& pos, Move m1, Move m2); - bool value_is_mate(Value value); Value value_to_tt(Value v, int ply); Value value_from_tt(Value v, int ply); bool ok_to_use_TT(const TTEntry* tte, Depth depth, Value beta, int ply); @@ -301,6 +301,7 @@ namespace { Value refine_eval(const TTEntry* tte, Value defaultEval, int ply); void update_history(const Position& pos, Move move, Depth depth, Move movesSearched[], int moveCount); void update_gains(const Position& pos, Move move, Value before, Value after); + void do_skill_level(Move* best, Move* ponder); int current_search_time(); std::string value_to_uci(Value v); @@ -514,7 +515,8 @@ bool think(Position& pos, bool infinite, bool ponder, int time[], int increment[ // Do we have to play with skill handicap? In this case enable MultiPV that // we will use behind the scenes to retrieve a set of possible moves. - MultiPV = (SkillLevel < 20 ? Max(UCIMultiPV, 4) : UCIMultiPV); + SkillLevelEnabled = (SkillLevel < 20); + MultiPV = (SkillLevelEnabled ? Max(UCIMultiPV, 4) : UCIMultiPV); // Set the number of active threads ThreadsMgr.read_uci_options(); @@ -611,16 +613,16 @@ namespace { SearchStack ss[PLY_MAX_PLUS_2]; Value bestValues[PLY_MAX_PLUS_2]; int bestMoveChanges[PLY_MAX_PLUS_2]; - int depth, aspirationDelta; + int depth, aspirationDelta, skillSamplingDepth; Value value, alpha, beta; - Move bestMove, easyMove; + Move bestMove, easyMove, skillBest, skillPonder; // Initialize stuff before a new search memset(ss, 0, 4 * sizeof(SearchStack)); TT.new_search(); H.clear(); - *ponderMove = bestMove = easyMove = MOVE_NONE; - depth = aspirationDelta = 0; + *ponderMove = bestMove = easyMove = skillBest = skillPonder = MOVE_NONE; + depth = aspirationDelta = skillSamplingDepth = 0; alpha = -VALUE_INFINITE, beta = VALUE_INFINITE; ss->currentMove = MOVE_NULL; // Hack to skip update_gains() @@ -637,6 +639,11 @@ namespace { return MOVE_NONE; } + // Choose a random sampling depth according to SkillLevel so that at low + // skills there is an higher risk to pick up a blunder. + if (SkillLevelEnabled) + skillSamplingDepth = 4 + SkillLevel + (RK.rand() % 4); + // Iterative deepening loop while (++depth <= PLY_MAX && (!MaxDepth || depth <= MaxDepth) && !StopRequest) { @@ -699,6 +706,10 @@ namespace { bestValues[depth] = value; bestMoveChanges[depth] = Rml.bestMoveChanges; + // Do we need to pick now the best and the ponder moves ? + if (SkillLevelEnabled && depth == skillSamplingDepth) + do_skill_level(&skillBest, &skillPonder); + // Send PV line to GUI and to log file for (int i = 0; i < Min(UCIMultiPV, (int)Rml.size()); i++) cout << Rml[i].pv_info_to_uci(pos, depth, alpha, beta, i) << endl; @@ -755,45 +766,14 @@ namespace { } } - // When playing with strength handicap choose best move among the MultiPV set - // using a statistical rule dependent on SkillLevel. Idea by Heinz van Saanen. - if (SkillLevel < 20) + // When using skills fake best and ponder moves with the sub-optimal ones + if (SkillLevelEnabled) { - assert(MultiPV > 1); - - // Rml list is already sorted by pv_score in descending order - int s; - int max_s = -VALUE_INFINITE; - int size = Min(MultiPV, (int)Rml.size()); - int max = Rml[0].pv_score; - int var = Min(max - Rml[size - 1].pv_score, PawnValueMidgame); - int wk = 120 - 2 * SkillLevel; - - // PRNG sequence should be non deterministic - for (int i = abs(get_system_time() % 50); i > 0; i--) - RK.rand(); - - // Choose best move. For each move's score we add two terms both dependent - // on wk, one deterministic and bigger for weaker moves, and one random, - // then we choose the move with the resulting highest score. - for (int i = 0; i < size; i++) - { - s = Rml[i].pv_score; + if (skillBest == MOVE_NONE) // Still unassigned ? + do_skill_level(&skillBest, &skillPonder); - // Don't allow crazy blunders even at very low skills - if (i > 0 && Rml[i-1].pv_score > s + EasyMoveMargin) - break; - - // This is our magical formula - s += ((max - s) * wk + var * (RK.rand() % wk)) / 128; - - if (s > max_s) - { - max_s = s; - bestMove = Rml[i].pv[0]; - *ponderMove = Rml[i].pv[1]; - } - } + bestMove = skillBest; + *ponderMove = skillPonder; } return bestMove; @@ -826,7 +806,7 @@ namespace { ValueType vt; Value bestValue, value, oldAlpha; Value refinedValue, nullValue, futilityBase, futilityValueScaled; // Non-PV specific - bool isPvMove, isCheck, singularExtensionNode, moveIsCheck, captureOrPromotion, dangerous; + bool isPvMove, isCheck, singularExtensionNode, moveIsCheck, captureOrPromotion, dangerous, isBadCap; bool mateThreat = false; int moveCount = 0, playedMoveCount = 0; int threadID = pos.thread(); @@ -921,7 +901,7 @@ namespace { && !isCheck && refinedValue < beta - razor_margin(depth) && ttMove == MOVE_NONE - && !value_is_mate(beta) + && abs(beta) < VALUE_MATE_IN_PLY_MAX && !pos.has_pawn_on_7th(pos.side_to_move())) { Value rbeta = beta - razor_margin(depth); @@ -940,7 +920,7 @@ namespace { && depth < RazorDepth && !isCheck && refinedValue >= beta + futility_margin(depth, 0) - && !value_is_mate(beta) + && abs(beta) < VALUE_MATE_IN_PLY_MAX && pos.non_pawn_material(pos.side_to_move())) return refinedValue - futility_margin(depth, 0); @@ -950,7 +930,7 @@ namespace { && depth > ONE_PLY && !isCheck && refinedValue >= beta - && !value_is_mate(beta) + && abs(beta) < VALUE_MATE_IN_PLY_MAX && pos.non_pawn_material(pos.side_to_move())) { ss->currentMove = MOVE_NULL; @@ -971,7 +951,7 @@ namespace { if (nullValue >= beta) { // Do not return unproven mate scores - if (nullValue >= value_mate_in(PLY_MAX)) + if (nullValue >= VALUE_MATE_IN_PLY_MAX) nullValue = beta; if (depth < 6 * ONE_PLY) @@ -1132,7 +1112,7 @@ split_point_start: // At split points actual search starts from here // Move count based pruning if ( moveCount >= futility_move_count(depth) && !(threatMove && connected_threat(pos, move, threatMove)) - && bestValue > value_mated_in(PLY_MAX)) // FIXME bestValue is racy + && bestValue > VALUE_MATED_IN_PLY_MAX) // FIXME bestValue is racy { if (SpNode) lock_grab(&(sp->lock)); @@ -1163,7 +1143,7 @@ split_point_start: // At split points actual search starts from here // Prune moves with negative SEE at low depths if ( predictedDepth < 2 * ONE_PLY - && bestValue > value_mated_in(PLY_MAX) + && bestValue > VALUE_MATED_IN_PLY_MAX && pos.see_sign(move) < 0) { if (SpNode) @@ -1173,6 +1153,16 @@ split_point_start: // At split points actual search starts from here } } + // Bad capture detection. Will be used by prob-cut search + isBadCap = depth >= 3 * ONE_PLY + && depth < 8 * ONE_PLY + && captureOrPromotion + && move != ttMove + && !dangerous + && !move_is_promotion(move) + && abs(alpha) < VALUE_MATE_IN_PLY_MAX + && pos.see_sign(move) < 0; + // Step 13. Make the move pos.do_move(move, st, ci, moveIsCheck); @@ -1194,6 +1184,7 @@ split_point_start: // At split points actual search starts from here // Step 14. Reduced depth search // If the move fails high will be re-searched at full depth. bool doFullDepthSearch = true; + alpha = SpNode ? sp->alpha : alpha; if ( depth >= 3 * ONE_PLY && !captureOrPromotion @@ -1214,6 +1205,18 @@ split_point_start: // At split points actual search starts from here ss->reduction = DEPTH_ZERO; // Restore original reduction } + // Probcut search for bad captures. If a reduced search returns a value + // very below beta then we can (almost) safely prune the bad capture. + if (isBadCap) + { + ss->reduction = 3 * ONE_PLY; + Value redAlpha = alpha - 300; + Depth d = newDepth - ss->reduction; + value = -search(pos, ss+1, -(redAlpha+1), -redAlpha, d, ply+1); + doFullDepthSearch = (value > redAlpha); + ss->reduction = DEPTH_ZERO; // Restore original reduction + } + // Step 15. Full depth search if (doFullDepthSearch) { @@ -1495,7 +1498,7 @@ split_point_start: // At split points actual search starts from here // Detect non-capture evasions that are candidate to be pruned evasionPrunable = isCheck - && bestValue > value_mated_in(PLY_MAX) + && bestValue > VALUE_MATED_IN_PLY_MAX && !pos.move_is_capture(move) && !pos.can_castle(pos.side_to_move()); @@ -1669,28 +1672,16 @@ split_point_start: // At split points actual search starts from here } - // value_is_mate() checks if the given value is a mate one eventually - // compensated for the ply. - - bool value_is_mate(Value value) { - - assert(abs(value) <= VALUE_INFINITE); - - return value <= value_mated_in(PLY_MAX) - || value >= value_mate_in(PLY_MAX); - } - - // value_to_tt() adjusts a mate score from "plies to mate from the root" to // "plies to mate from the current ply". Non-mate scores are unchanged. // The function is called before storing a value to the transposition table. Value value_to_tt(Value v, int ply) { - if (v >= value_mate_in(PLY_MAX)) + if (v >= VALUE_MATE_IN_PLY_MAX) return v + ply; - if (v <= value_mated_in(PLY_MAX)) + if (v <= VALUE_MATED_IN_PLY_MAX) return v - ply; return v; @@ -1702,10 +1693,10 @@ split_point_start: // At split points actual search starts from here Value value_from_tt(Value v, int ply) { - if (v >= value_mate_in(PLY_MAX)) + if (v >= VALUE_MATE_IN_PLY_MAX) return v - ply; - if (v <= value_mated_in(PLY_MAX)) + if (v <= VALUE_MATED_IN_PLY_MAX) return v + ply; return v; @@ -1762,15 +1753,6 @@ split_point_start: // At split points actual search starts from here *dangerous = true; } - if ( PvNode - && captureOrPromotion - && pos.type_of_piece_on(move_to(m)) != PAWN - && pos.see_sign(m) >= 0) - { - result += ONE_PLY / 2; - *dangerous = true; - } - return Min(result, ONE_PLY); } @@ -1824,8 +1806,8 @@ split_point_start: // At split points actual search starts from here Value v = value_from_tt(tte->value(), ply); return ( tte->depth() >= depth - || v >= Max(value_mate_in(PLY_MAX), beta) - || v < Min(value_mated_in(PLY_MAX), beta)) + || v >= Max(VALUE_MATE_IN_PLY_MAX, beta) + || v < Min(VALUE_MATED_IN_PLY_MAX, beta)) && ( ((tte->type() & VALUE_TYPE_LOWER) && v >= beta) || ((tte->type() & VALUE_TYPE_UPPER) && v < beta)); @@ -1907,7 +1889,7 @@ split_point_start: // At split points actual search starts from here if (abs(v) < VALUE_MATE - PLY_MAX * ONE_PLY) s << "cp " << int(v) * 100 / int(PawnValueMidgame); // Scale to centipawns else - s << "mate " << (v > 0 ? (VALUE_MATE - v + 1) / 2 : -(VALUE_MATE + v) / 2); + s << "mate " << (v > 0 ? VALUE_MATE - v + 1 : -VALUE_MATE - v) / 2; return s.str(); } @@ -1944,10 +1926,7 @@ split_point_start: // At split points actual search starts from here // We are line oriented, don't read single chars std::string command; - if (!std::getline(std::cin, command)) - command = "quit"; - - if (command == "quit") + if (!std::getline(std::cin, command) || command == "quit") { // Quit the program as soon as possible Pondering = false; @@ -2025,20 +2004,12 @@ split_point_start: // At split points actual search starts from here std::string command; - while (true) - { - // Wait for a command from stdin - if (!std::getline(std::cin, command)) - command = "quit"; + // Wait for a command from stdin + while ( std::getline(std::cin, command) + && command != "ponderhit" && command != "stop" && command != "quit") {}; - if (command == "quit") - { - QuitRequest = true; - break; - } - else if (command == "ponderhit" || command == "stop") - break; - } + if (command != "ponderhit" && command != "stop") + QuitRequest = true; // Must be "quit" or getline() returned false } @@ -2604,4 +2575,46 @@ split_point_start: // At split points actual search starts from here } } + + // When playing with strength handicap choose best move among the MultiPV set + // using a statistical rule dependent on SkillLevel. Idea by Heinz van Saanen. + void do_skill_level(Move* best, Move* ponder) { + + assert(MultiPV > 1); + + // Rml list is already sorted by pv_score in descending order + int s; + int max_s = -VALUE_INFINITE; + int size = Min(MultiPV, (int)Rml.size()); + int max = Rml[0].pv_score; + int var = Min(max - Rml[size - 1].pv_score, PawnValueMidgame); + int wk = 120 - 2 * SkillLevel; + + // PRNG sequence should be non deterministic + for (int i = abs(get_system_time() % 50); i > 0; i--) + RK.rand(); + + // Choose best move. For each move's score we add two terms both dependent + // on wk, one deterministic and bigger for weaker moves, and one random, + // then we choose the move with the resulting highest score. + for (int i = 0; i < size; i++) + { + s = Rml[i].pv_score; + + // Don't allow crazy blunders even at very low skills + if (i > 0 && Rml[i-1].pv_score > s + EasyMoveMargin) + break; + + // This is our magical formula + s += ((max - s) * wk + var * (RK.rand() % wk)) / 128; + + if (s > max_s) + { + max_s = s; + *best = Rml[i].pv[0]; + *ponder = Rml[i].pv[1]; + } + } + } + } // namespace