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@ -28,8 +28,8 @@ obj/%.o:
@mkdir -p $(dir $@)
$(CXX) $(CXXFLAGS) -o $@ -c $<
stickfosh: $(OBJFILES)
$(CXX) $(CXXFLAGS) $(OBJFILES) $(LOADLIBES) $(LDLIBS) -o stickfosh -lsfml-graphics -lsfml-window -lsfml-system
main: $(OBJFILES)
$(CXX) $(CXXFLAGS) $(OBJFILES) $(LOADLIBES) $(LDLIBS) -o main -lsfml-graphics -lsfml-window -lsfml-system
clean:
rm -rf obj/* $(DEPFILES) test_bin/

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@ -1,86 +1,3 @@
# Stickfosh
> [Stockfish](https://stockfishchess.org), but worse :)
## Overview
This project is a **Chess AI** built using the **Model-View-Controller (MVC)
pattern**. It provides a modular framework for playing chess, allowing both
**human vs AI** and **AI vs AI** matches. The AI has undergone several
iterations, improving its decision-making capabilities through enhancements like
**alpha-beta pruning**, **move ordering**, **iterative deepening**, and
**transposition tables**.
## Features
- **MVC Architecture**: The project follows the MVC pattern for clean separation of concerns:
- **Model**: Handles chess rules, board state, and AI logic.
- **View**: GUI and NoOp (that shows nothing, but is useful for debugging the AIs) rendering options.
- **Controller**: Manages interactions between players and the game.
- **Multiple AI Versions**: Several AI versions with increasing complexity have been implemented.
- **AI vs AI Matches**: A dedicated mode to watch different AI versions compete.
- **Human vs AI Mode**: Play against the AI using a graphical interface.
- **FEN Support**: Load chess positions using FEN notation.
- **Performance Testing (Perft)**: Built-in performance testing for move generation.
## AI Iterations
This project has undergone multiple AI improvements, including:
1. **v0 Random AI**: Selects moves randomly.
1. **v1 Pure Minimax**: Implements basic minimax search.
1. **v2 Alpha-Beta Pruning**: Optimizes minimax with pruning.
1. **v3 Move Ordering**: Prioritizes moves to improve search efficiency.
1. **v4 Search Captures**: Enhances move ordering by focusing on captures.
1. **v5 Better Endgame**: Introduces heuristics for endgame play.
1. **v6 Iterative Deepening**: Dynamically adjusts search depth for better performance.
1. **v7 Transposition Tables**: Caches board states to reduce redundant computations.
## Installation & Usage
### Prerequisites
- C++ Compiler (C++17 or later)
- `make`
- SFML (for GUI rendering)
### Build Instructions
1. Clone the repository:
```sh
git clone https://github.com/karma-riuk/stickfosh.git
cd stickfosh
```
1. Create a build directory and compile:
```sh
make stickfosh
```
1. Run the program:
```sh
./stickfosh
```
## Running the Application
Stickfosh provides multiple execution modes, selectable via command-line arguments:
| Mode | Description | Example Command |
|------|------------|----------------|
| **Human vs AI** | Play against the AI | `./stickfosh --mode human_vs_ai` |
| **AI vs AI** | Watch two AI versions compete | `./stickfosh --mode ai_vs_ai --ai1 v3_AB_ordering --ai2 v6_iterative_deepening` |
| **Human vs Human** | Manually input moves for both sides | `./stickfosh --mode human_vs_human` |
| **Perft Testing** | Performance test move generation | `./stickfosh --mode perft` |
| **Custom FEN** | Start from a custom position | `./stickfosh --mode ai_vs_ai --fen "rnbqkb1r/pppppppp/8/8/8/8/PPPPPPPP/RNBQKB1R w KQkq - 0 1"` |
## Video Demo
<!-- [![AI vs AI Chess Match](https://img.youtube.com/vi/XXXXXXXXXX/0.jpg)](https://www.youtube.com/watch?v=XXXXXXXXXX) -->
<!-- *Click the image above to watch a video of two AI versions competing!* -->
## Future Improvements
- Implement **opening book** for better early-game decisions.
- Enhance **evaluation function** with more advanced heuristics.
- Introduce **neural network-based AI** for machine-learning-driven play.
- Introduce **Monte-Carlo Tree Search** for stochastic driven play.
> Stockfish, but worse :)

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@ -9,73 +9,36 @@
#include "view/view.hpp"
#include <chrono>
#include <iostream>
#include <string>
void print_usage() {
std::cout << "Usage: chess_ai [OPTIONS]\n";
std::cout << "Options:\n";
std::cout
<< " --mode <human_vs_ai|ai_vs_ai|human_vs_human|perft> Choose the "
"game mode.\n";
std::cout << " --ai1 <version> Choose the first AI version (for ai_vs_ai "
"mode).\n";
std::cout << " --ai2 <version> Choose the second AI version (for "
"ai_vs_ai mode).\n";
std::cout << " --fen <FEN_STRING> Set a custom FEN position.\n";
std::cout << " --help Show this help message.\n";
}
int main(int argc, char* argv[]) {
std::string mode = "human_vs_ai";
std::string ai1_version = "v0_random";
std::string ai2_version = "v6_iterative_deepening";
std::string fen =
"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1";
// std::string pos =
// "r2qkb1r/2p1pppp/p1n1b3/1p6/B2P4/2P1P3/P4PPP/R1BQK1NR w KQkq - 0 9 ";
std::string pos = "3r4/3r4/3k4/8/3K4/8/8/8 w - - 0 1";
for (int i = 1; i < argc; ++i) {
std::string arg = argv[i];
if (arg == "--mode" && i + 1 < argc) {
mode = argv[++i];
} else if (arg == "--ai1" && i + 1 < argc) {
ai1_version = argv[++i];
} else if (arg == "--ai2" && i + 1 < argc) {
ai2_version = argv[++i];
} else if (arg == "--fen" && i + 1 < argc) {
fen = argv[++i];
} else if (arg == "--help") {
print_usage();
return 0;
} else {
std::cerr << "Unknown option: " << arg << "\n";
print_usage();
return 1;
}
}
// pos for ai timing<
// std::string pos =
// "r3k2r/p1ppqpb1/Bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPB1PPP/R3K2R b KQkq - 0
// 3";
Board board = Board::setup_fen_position(fen);
GUI gui;
Controller* controller = nullptr;
Board b = Board::setup_fen_position(pos);
if (mode == "human_vs_ai") {
ai::v6_iterative_deepening ai(false, std::chrono::milliseconds(2000));
controller = new HumanVsAIController(board, gui, ai);
} else if (mode == "ai_vs_ai") {
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::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));
controller = new AIvsAIController(board, gui, p1, p2);
} else if (mode == "human_vs_human") {
controller = new ManualController(board, gui);
} else if (mode == "perft") {
perft();
return 0;
} else {
std::cerr << "Invalid mode selected!\n";
print_usage();
return 1;
}
controller->start();
delete controller;
GUI gui;
// NoOpView gui;
// AIvsAIController manual(b, gui, p1, p2);
HumanVsAIController manual(b, gui, p2);
Controller& controller = manual;
controller.start();
// perft();
return 0;
}

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

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@ -20,9 +20,8 @@ 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 {
@ -30,7 +29,7 @@ namespace ai {
Move _search(const Board&) override;
int _eval(const Board&) override {
int eval(const Board&) override {
return 0;
};
};
@ -42,7 +41,7 @@ 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 {
@ -53,7 +52,7 @@ namespace ai {
v2_alpha_beta(bool w, std::chrono::milliseconds tt): AI(w, tt) {}
virtual Move _search(const Board&) override;
virtual int _eval(const Board&) override;
virtual int eval(const Board&) override;
};
class v3_AB_ordering : public AI {
@ -64,7 +63,7 @@ namespace ai {
v3_AB_ordering(bool w, std::chrono::milliseconds tt): AI(w, tt) {}
virtual Move _search(const Board&) override;
virtual int _eval(const Board&) override;
virtual int eval(const Board&) override;
};
class v4_search_captures : public v3_AB_ordering {
@ -87,7 +86,7 @@ namespace ai {
v5_better_endgame(bool w, std::chrono::milliseconds tt)
: v4_search_captures(w, tt) {}
virtual int _eval(const Board&) override;
virtual int eval(const Board&) override;
};
class v6_iterative_deepening : public v5_better_endgame {

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@ -7,7 +7,10 @@
#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;
@ -49,6 +52,7 @@ Move ai::v1_pure_minimax::_search(const Board& b) {
}
}
#endif
std::cout << "Looked at " << position_counter << " positions" << std::endl;
return best_move;
}
@ -74,7 +78,8 @@ 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);

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@ -7,7 +7,11 @@
#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;
@ -49,6 +53,7 @@ Move ai::v2_alpha_beta::_search(const Board& b) {
}
}
#endif
std::cout << "Looked at " << position_counter << " positions" << std::endl;
return best_move;
}
@ -74,7 +79,8 @@ 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);

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@ -8,14 +8,12 @@
#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();
std::sort(moves.begin(), moves.end(), [&](Move& m1, Move& m2) {
int score = m1.score_guess(b) - m2.score_guess(b);
if (!am_white)
score *= -1;
return score < 0;
});
Move best_move;
int best_eval = -INFINITY;
@ -58,6 +56,7 @@ Move ai::v3_AB_ordering::_search(const Board& b) {
}
}
#endif
std::cout << "Looked at " << position_counter << " positions" << std::endl;
return best_move;
}
@ -75,10 +74,7 @@ int ai::v3_AB_ordering::_ab_search(
std::vector<Move> moves = b.all_legal_moves();
std::sort(moves.begin(), moves.end(), [&](Move& m1, Move& m2) {
int score = m1.score_guess(b) - m2.score_guess(b);
if (!am_white)
score *= -1;
return score < 0;
return m1.score_guess(b) > m2.score_guess(b);
});
Move best_move;
@ -92,7 +88,8 @@ int ai::v3_AB_ordering::_ab_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);

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@ -6,6 +6,9 @@
#define MULTITHREADED 1
static int position_counter;
int ai::v4_search_captures::_ab_search(
const Board& b, int depth, int alpha, int beta
) {

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@ -36,8 +36,8 @@ static float endgame_phase_weight(int material_count_no_pawns) {
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);
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

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@ -13,8 +13,7 @@ Move ai::v6_iterative_deepening::_search(const Board& b) {
int best_eval = -INFINITY;
std::map<Move, std::future<int>> futures;
int depth;
for (depth = 1; !stop_computation; depth++) {
for (int depth = 1; !stop_computation; depth++) {
for (const Move& move : moves) {
Board tmp_board = b.make_move(move);
futures.insert(
@ -37,7 +36,5 @@ Move ai::v6_iterative_deepening::_search(const Board& b) {
}
futures.clear();
}
std::cout << "Went up until depth: " << depth << std::endl;
return best_move;
}

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@ -24,31 +24,3 @@ int Move::score_guess(const Board& b) const {
return ret;
}
Move Move::from_string(std::string move) {
if (!(4 <= move.size() && move.size() <= 5))
throw std::invalid_argument("Move must be 4 or 5 characters long");
Move ret;
ret.source_square = Coords::from_algebraic(move.substr(0, 2)).to_index();
ret.target_square = Coords::from_algebraic(move.substr(2, 2)).to_index();
if (move.size() == 5)
switch (move[4]) {
case 'n':
ret.promoting_to = Knigt;
break;
case 'b':
ret.promoting_to = Bishop;
break;
case 'r':
ret.promoting_to = Rook;
break;
case 'q':
ret.promoting_to = Queen;
break;
default:
throw std::invalid_argument("Promotion piece must be one of 'nbrq'"
);
}
ret.target_square = Coords::from_algebraic(move.substr(2, 2)).to_index();
return ret;
}

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@ -15,8 +15,6 @@ struct Move {
int score_guess(const Board&) const;
static Move from_string(std::string);
std::string to_string() const {
std::stringstream ss;
ss << Coords::from_index(source_square)

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@ -1,13 +0,0 @@
#include "../src/model/board/board.hpp"
#include "lib.hpp"
int main() {
std::string str_move = "a2a3";
ASSERT_EQUALS(str_move, Move::from_string(str_move).to_string());
str_move = "b2f4";
ASSERT_EQUALS(str_move, Move::from_string(str_move).to_string());
str_move = "a2a1r";
ASSERT_EQUALS(str_move, Move::from_string(str_move).to_string());
}