generalized the position counter

thinking time runs out

src/main.cpp

@@ -13,24 +13,27 @@
 int main(int argc, char* argv[]) {
     // std::string pos =
     //     "r2qkb1r/2p1pppp/p1n1b3/1p6/B2P4/2P1P3/P4PPP/R1BQK1NR w KQkq - 0 9 ";
-    // std::string pos = "8/6K1/5P2/8/1k6/8/8/8 w - - 0 1";
+    std::string pos = "3r4/3r4/3k4/8/3K4/8/8/8 w - - 0 1";
 
     // pos for ai timing<
-    std::string pos =
+    // std::string pos =
-        "r3k2r/p1ppqpb1/Bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPB1PPP/R3K2R b KQkq - 0 3";
+    //     "r3k2r/p1ppqpb1/Bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPB1PPP/R3K2R b KQkq - 0
+    //     3";
 
     Board b = Board::setup_fen_position(pos);
 
     ai::v0_random p1(true, std::chrono::milliseconds(1000));
     // ai::v1_pure_minimax p2(false, std::chrono::milliseconds(20000));
     // ai::v2_alpha_beta p2(false, std::chrono::milliseconds(20000));
-    ai::v3_AB_ordering p2(false, std::chrono::milliseconds(20000));
+    // ai::v3_AB_ordering p2(false, std::chrono::milliseconds(20000));
     // ai::v4_search_captures p2(false, std::chrono::milliseconds(20000));
+    // ai::v5_better_endgame p2(false, std::chrono::milliseconds(20000));
+    ai::v6_iterative_deepening p2(false, std::chrono::milliseconds(2000));
 
-    // GUI gui;
+    GUI gui;
-    NoOpView gui;
+    // NoOpView gui;
-    AIvsAIController manual(b, gui, p1, p2);
+    // AIvsAIController manual(b, gui, p1, p2);
-    // HumanVsAIController manual(b, gui, p2);
+    HumanVsAIController manual(b, gui, p2);
 
     Controller& controller = manual;
 

src/model/ais/ai.cpp

@@ -4,7 +4,10 @@
 #include <ostream>
 #include <thread>
 
+static long int position_counter = 0;
+
 Move ai::AI::search(const Board& b) {
+    position_counter = 0;
     Move result;
 
     std::condition_variable cv;
@@ -47,6 +50,13 @@ Move ai::AI::search(const Board& b) {
     // Ensure timer thread is also stopped
     timer_thread.join();
 
-    std::cout << "Took " << elapsed << " ms" << std::endl;
+    std::cout << "Took " << elapsed << " ms, " << "Looked at "
+              << position_counter << " positions" << std::endl;
     return result;
 }
+
+int ai::AI::eval(const Board& b) {
+    int ret = _eval(b);
+    position_counter++;
+    return ret;
+}

src/model/ais/ai.hpp

@@ -20,8 +20,9 @@ namespace ai {
         std::atomic<bool> stop_computation = false;
 
         Move search(const Board& b);
+        int eval(const Board&);
 
-        virtual int eval(const Board&) = 0;
+        virtual int _eval(const Board&) = 0;
     };
 
     struct v0_random : public AI {
@@ -29,7 +30,7 @@ namespace ai {
 
         Move _search(const Board&) override;
 
-        int eval(const Board&) override {
+        int _eval(const Board&) override {
             return 0;
         };
     };
@@ -41,39 +42,63 @@ namespace ai {
         v1_pure_minimax(bool w, std::chrono::milliseconds tt): AI(w, tt) {}
 
         Move _search(const Board&) override;
-        int eval(const Board&) override;
+        int _eval(const Board&) override;
     };
 
     class v2_alpha_beta : public AI {
         // looks two moves ahead, with alpha-beta pruning (no move ordering)
-        int _search(const Board&, int, int, int);
+        virtual int _search(const Board&, int, int, int);
 
       public:
         v2_alpha_beta(bool w, std::chrono::milliseconds tt): AI(w, tt) {}
 
-        Move _search(const Board&) override;
+        virtual Move _search(const Board&) override;
-        int eval(const Board&) override;
+        virtual int _eval(const Board&) override;
     };
 
     class v3_AB_ordering : public AI {
         // looks two moves ahead, with alpha-beta pruning, with move ordering
-        virtual int _search(const Board&, int, int, int);
+        virtual int _ab_search(const Board&, int, int, int);
 
       public:
         v3_AB_ordering(bool w, std::chrono::milliseconds tt): AI(w, tt) {}
 
-        Move _search(const Board&) override;
+        virtual Move _search(const Board&) override;
-        int eval(const Board&) override;
+        virtual int _eval(const Board&) override;
     };
 
     class v4_search_captures : public v3_AB_ordering {
+      protected:
         // same as v3, but looking at only at captures when leaf is reached,
         // until no captures are left
-        int _search(const Board&, int, int, int) override;
+        virtual int _ab_search(const Board&, int, int, int) override;
-        int _search_captures(const Board&, int, int);
+        virtual int _search_captures(const Board&, int, int);
 
       public:
         v4_search_captures(bool w, std::chrono::milliseconds tt)
             : v3_AB_ordering(w, tt) {}
     };
+
+    class v5_better_endgame : public v4_search_captures {
+        // same as v4, but with a better evaluation function, that forces the
+        // king towards the corner of the board for endgames
+
+      public:
+        v5_better_endgame(bool w, std::chrono::milliseconds tt)
+            : v4_search_captures(w, tt) {}
+
+        virtual int _eval(const Board&) override;
+    };
+
+    class v6_iterative_deepening : public v5_better_endgame {
+        // same as v5, but instead of just looking 2 moves ahead, it does
+        // iterative depening until and keeps on searching until the thinking
+        // time runs out
+
+      public:
+        v6_iterative_deepening(bool w, std::chrono::milliseconds tt)
+            : v5_better_endgame(w, tt) {}
+
+        virtual Move _search(const Board&) override;
+    };
 } // namespace ai

src/model/ais/v1_pure_minimax.cpp

@@ -7,10 +7,7 @@
 
 #define MULTITHREADED 1
 
-static int position_counter = 0;
-
 Move ai::v1_pure_minimax::_search(const Board& b) {
-    position_counter = 0;
     std::vector<Move> moves = b.all_legal_moves();
 
     Move best_move;
@@ -52,7 +49,6 @@ Move ai::v1_pure_minimax::_search(const Board& b) {
         }
     }
 #endif
-    std::cout << "Looked at " << position_counter << " positions" << std::endl;
     return best_move;
 }
 
@@ -78,8 +74,7 @@ int ai::v1_pure_minimax::_search(const Board& b, int depth) {
     return best_evaluation;
 }
 
-int ai::v1_pure_minimax::eval(const Board& b) {
+int ai::v1_pure_minimax::_eval(const Board& b) {
-    position_counter++;
     int white_eval = count_material(b, Colour::White);
     int black_eval = count_material(b, Colour::Black);
 

src/model/ais/v2_alpha_beta.cpp

@@ -7,11 +7,7 @@
 
 #define MULTITHREADED 1
 
-
-static int position_counter = 0;
-
 Move ai::v2_alpha_beta::_search(const Board& b) {
-    position_counter = 0;
     std::vector<Move> moves = b.all_legal_moves();
 
     Move best_move;
@@ -53,7 +49,6 @@ Move ai::v2_alpha_beta::_search(const Board& b) {
         }
     }
 #endif
-    std::cout << "Looked at " << position_counter << " positions" << std::endl;
     return best_move;
 }
 
@@ -79,8 +74,7 @@ int ai::v2_alpha_beta::_search(const Board& b, int depth, int alpha, int beta) {
     return alpha;
 }
 
-int ai::v2_alpha_beta::eval(const Board& b) {
+int ai::v2_alpha_beta::_eval(const Board& b) {
-    position_counter++;
     int white_eval = count_material(b, Colour::White);
     int black_eval = count_material(b, Colour::Black);
 

src/model/ais/v3_AB_ordering.cpp

@@ -8,11 +8,7 @@
 
 #define MULTITHREADED 1
 
-
-static int position_counter;
-
 Move ai::v3_AB_ordering::_search(const Board& b) {
-    position_counter = 0;
     std::vector<Move> moves = b.all_legal_moves();
 
     Move best_move;
@@ -25,9 +21,11 @@ Move ai::v3_AB_ordering::_search(const Board& b) {
     std::map<Move, std::future<int>> futures;
     for (const Move& move : moves) {
         Board tmp_board = b.make_move(move);
-        futures.insert({move, pool.enqueue([&, tmp_board]() {
+        futures.insert(
-                            return _search(tmp_board, 3, -INFINITY, INFINITY);
+            {move, pool.enqueue([&, tmp_board]() {
-                        })});
+                 return _ab_search(tmp_board, 3, -INFINITY, INFINITY);
+             })}
+        );
     }
 
     int counter = 0;
@@ -54,11 +52,10 @@ Move ai::v3_AB_ordering::_search(const Board& b) {
         }
     }
 #endif
-    std::cout << "Looked at " << position_counter << " positions" << std::endl;
     return best_move;
 }
 
-int ai::v3_AB_ordering::_search(
+int ai::v3_AB_ordering::_ab_search(
     const Board& b, int depth, int alpha, int beta
 ) {
     if (depth == 0 || stop_computation)
@@ -78,7 +75,7 @@ int ai::v3_AB_ordering::_search(
     Move best_move;
     for (const Move& move : moves) {
         Board tmp_board = b.make_move(move);
-        int tmp_eval = -_search(tmp_board, depth - 1, -beta, -alpha);
+        int tmp_eval = -_ab_search(tmp_board, depth - 1, -beta, -alpha);
         if (tmp_eval >= beta)
             return beta;
         alpha = std::max(alpha, tmp_eval);
@@ -86,8 +83,7 @@ int ai::v3_AB_ordering::_search(
     return alpha;
 }
 
-int ai::v3_AB_ordering::eval(const Board& b) {
+int ai::v3_AB_ordering::_eval(const Board& b) {
-    position_counter++;
     int white_eval = count_material(b, Colour::White);
     int black_eval = count_material(b, Colour::Black);
 

src/model/ais/v4_search_captures.cpp

@@ -6,10 +6,7 @@
 
 #define MULTITHREADED 1
 
-
+int ai::v4_search_captures::_ab_search(
-static int position_counter;
-
-int ai::v4_search_captures::_search(
     const Board& b, int depth, int alpha, int beta
 ) {
     if (depth == 0 || stop_computation)
@@ -29,7 +26,7 @@ int ai::v4_search_captures::_search(
     Move best_move;
     for (const Move& move : moves) {
         Board tmp_board = b.make_move(move);
-        int tmp_eval = -_search(tmp_board, depth - 1, -beta, -alpha);
+        int tmp_eval = -_ab_search(tmp_board, depth - 1, -beta, -alpha);
         if (tmp_eval >= beta)
             return beta;
         alpha = std::max(alpha, tmp_eval);

src/model/ais/v5_better_endgames.cpp

@@ -0,0 +1,52 @@
+#include "../pieces/piece.hpp"
+#include "../utils/utils.hpp"
+#include "ai.hpp"
+
+#include <algorithm>
+
+static int force_king_to_corner(
+    int8_t attacking_king, int8_t defending_king, float endgame_weight
+) {
+    int eval = 0;
+    Coords def_xy = Coords::from_index(defending_king);
+
+    Coords def_dist_to_center{
+        std::max(3 - def_xy.x, def_xy.x - 4),
+        std::max(3 - def_xy.y, def_xy.y - 4)
+    };
+    eval += def_dist_to_center.x + def_dist_to_center.y;
+
+    // make attacking king go closer to defending king to cut off escape routes
+    Coords attack_xy = Coords::from_index(attacking_king);
+    Coords dist_between_kings{
+        std::abs(attack_xy.x - def_xy.x),
+        std::abs(attack_xy.y - def_xy.y)
+    };
+    int distance = dist_between_kings.x + dist_between_kings.y;
+    eval += 14 - distance;
+
+    return (int) (eval * 10 * endgame_weight);
+}
+
+static float endgame_phase_weight(int material_count_no_pawns) {
+    static int endgame_material_start =
+        RookValue * 2 + BishopValue + KnightValue;
+
+    float multiplier = 1.f / endgame_material_start;
+    return 1.f - std::min(1.f, material_count_no_pawns * multiplier);
+}
+
+int ai::v5_better_endgame::_eval(const Board& b) {
+    int old_eval = v4_search_captures::_eval(b);
+    Colour attacking_colour = b.white_to_play ? White : Black;
+    Colour defending_colour = b.white_to_play ? Black : White;
+    return old_eval
+         + force_king_to_corner(
+               b.get_king_of(attacking_colour),
+               b.get_king_of(defending_colour),
+               endgame_phase_weight(
+                   count_material(b, attacking_colour, false)
+                   + count_material(b, defending_colour, false)
+               )
+         );
+}

src/model/ais/v6_iterative_deepening.cpp

@@ -0,0 +1,42 @@
+#include "../pieces/piece.hpp"
+#include "../utils/threadpool.hpp"
+#include "../utils/utils.hpp"
+#include "ai.hpp"
+
+#include <map>
+
+static int position_counter = 0;
+
+Move ai::v6_iterative_deepening::_search(const Board& b) {
+    ThreadPool pool(std::thread::hardware_concurrency());
+    std::vector<Move> moves = b.all_legal_moves();
+
+    Move best_move;
+    int best_eval = -INFINITY;
+
+    std::map<Move, std::future<int>> futures;
+    for (int depth = 1; !stop_computation; depth++) {
+        for (const Move& move : moves) {
+            Board tmp_board = b.make_move(move);
+            futures.insert(
+                {move, pool.enqueue([&, tmp_board]() {
+                     return _ab_search(tmp_board, depth, -INFINITY, INFINITY);
+                 })}
+            );
+        }
+
+        int counter = 0;
+        for (auto& [move, future] : futures) {
+            int eval = future.get();
+            counter++;
+            if (!am_white)
+                eval *= -1;
+            if (eval > best_eval) {
+                best_eval = eval;
+                best_move = move;
+            }
+        }
+        futures.clear();
+    }
+    return best_move;
+}

src/model/board/board.hpp

@@ -8,7 +8,6 @@
 
 struct Board {
   private:
-    int8_t get_king_of(int8_t) const;
     bool _no_legal_moves_for(Colour) const;
     bool _is_check_for(Colour) const;
     bool nlm = false, check = false;
@@ -25,6 +24,7 @@ struct Board {
 
     static Board setup_fen_position(std::string fen);
 
+    int8_t get_king_of(int8_t) const;
     Board skip_turn() const;
     Board make_move(Move, bool = true) const;
     std::string to_fen() const;

src/model/utils/utils.cpp

@@ -26,10 +26,13 @@ int piece_value(Piece p) {
     }
 }
 
-int count_material(const Board& b, int8_t colour) {
+int count_material(const Board& b, int8_t colour, bool count_pawns) {
     int ret = 0;
-    for (int i = 0; i < 64; i++)
+    for (int i = 0; i < 64; i++) {
+        if (b.piece_at(i) == Pawn && !count_pawns)
+            continue;
         if (b.colour_at(i) == colour)
             ret += piece_value(b.piece_at(i));
+    }
     return ret;
 }

src/model/utils/utils.hpp

@@ -7,7 +7,7 @@
 #include <vector>
 
 std::vector<int8_t> to_target_square(std::vector<Move>);
-int count_material(const Board&, int8_t);
+int count_material(const Board&, int8_t, bool = true);
 int piece_value(Piece);
 
 const int INFINITY = std::numeric_limits<int>::max();