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 (console-based) 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:

v0 Random AI: Selects moves randomly.
v1 Pure Minimax: Implements basic minimax search.
v2 Alpha-Beta Pruning: Optimizes minimax with pruning.
v3 Move Ordering: Prioritizes moves to improve search efficiency.
v4 Search Captures: Enhances move ordering by focusing on captures.
v5 Better Endgame: Introduces heuristics for endgame play.
v6 Iterative Deepening: Dynamically adjusts search depth for better performance.
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

Clone the repository:

git clone https://github.com/karma-riuk/stickfosh.git
cd stickfosh

Create a build directory and compile:
```
make main
```
Run the program:
```
./main
```

Running AI vs AI Matches

To watch two AI versions play against each other, modify main.cpp to instantiate the desired AI versions and run:

./main

Video Demo

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.