C++ Source Code Chess Game with OpenGL GUI and AI Implementation

C++ Source Code Chess Game with OpenGL GUI and AI Implementation

3DChessApp is a straightforward, cross-platform chess application featuring a 3D graphical user interface (GUI) and a fundamental AI opponent. Developed using modern C++11 standards, the application is compatible with both Windows and Linux operating systems, supporting 32-bit and 64-bit architectures. The project was designed to provide hands-on experience in building a small-scale software project collaboratively, from inception to completion, while exploring the methodologies employed in computer-based chess.

The final product is a functional chess game that offers various modes, including player vs. player, player vs. AI, and AI vs. AI. The engine largely adheres to FIDE chess rules with a few exceptions. Although the AI incorporates only a basic set of features compared to advanced chess engines, it delivers adequate challenge for casual human players.

Project Overview

The project has been divided into three major components, each designed to facilitate independent development and integration:

1. GUI

• 3D Interface: Utilizes OpenGL 2 for rendering.
• Menu System: Designed for user navigation and interaction.
• Model Loading: Integrated with Assimp for importing various model formats.
• Text Rendering: Employs FreeType2 for rendering text, including chess notation.

2. Chess Engine

• FIDE Compliant: Adheres to the FIDE rules for chess.
• Move Generation: Utilizes a bitboard-based approach.
• FEN Support: Includes functionality for FEN string input/output.
• AI Features:
  • Negamax Search: For evaluating game positions.
  • Alpha-Beta Pruning: To optimize search efficiency.
  • Material Evaluation: Basic scoring through material and piece square tables.
  • Zobrist Hashing: For efficient position hashing.
  • Polyglot Opening Book: Supports Polyglot format for opening book integration.
  • Transposition Tables: To store previously evaluated positions.
  • Iterative Deepening: For progressively deeper searches.
  • Configurable AI Profiles: Allows for different AI configurations.

3. Build Process

Windows

1. Checkout Boost: Retrieve Boost from SVN and place it in the 3rdparty directory.

2. Build Boost: Execute buildboost.bat in a Visual Studio 2013 command prompt (process may be lengthy).

3. Git Submodules:

   • Initialize with git submodule init.
   • Update with git submodule update.

4. Generate Project Files: Run runcmake.bat in the root directory.

5. Compile: Open the .sln project file in the build/ directory and build the solution.

   Build Requirements (Windows):

   • Visual Studio 2013
   • CMake 2.8.12 or higher
   • Python 2.7 or later
   • Boost 1.55 (trunk)

Linux

1. Git Submodules:

   • Initialize with git submodule init.
   • Update with git submodule update.

2. Generate Makefiles: Execute runcmake.sh.

3. Compile: Run make in the bin/ subfolder.

   Build Requirements (Linux):

   • GCC >= 4.8
   • pthreads
   • Python 2.7
   • Boost >= 1.54
   • For Ubuntu < 13.10: Use GCC >= 4.8 from PPA (e.g., ppa:ubuntu-toolchain-r/test).
   • For Ubuntu < 14.04: Use Boost >= 1.54 from PPA (e.g., ppa:boost-latest/ppa).