Pac-Man Game with Advanced AI Ghosts 👻

A modern C++ implementation of the classic Pac-Man game featuring intelligent ghost AI using different pathfinding algorithms. Built with SFML (Simple and Fast Multimedia Library) and advanced graph algorithms.

Project Decumentation

Project Video: [https://youtu.be/0M5LkG5rUYM)]

🎮 Features

Game Mechanics

• Classic Pac-Man gameplay with smooth movement
• Food collection system with score tracking
• Multiple ghost types with unique AI behaviors
• Anti-camping mechanisms to prevent ghost stalling
• Dynamic difficulty adjustment based on remaining food

Advanced Ghost AI

• BFS Ghost: Uses Breadth-First Search for direct pursuit with random movement elements
• A Ghost (A-Star Search) : Intelligent pursuit of Pac-Man using heuristic-guided pathfinding
• Dijkstra Ghost: Uses Dijkstra's algorithm with weighted edges based on food positions for optimal pathfinding

Technical Features

• Graph-based maze representation
• Multiple pathfinding algorithms (BFS, DFS, Dijkstra)
• Object-oriented design with inheritance and polymorphism
• Strategy pattern for AI behavior
• SFML graphics with sprite animation

🛠️ Technologies Used

• C++17 - Core programming language
• SFML 2.5+ - Graphics, audio, and input handling
• Graph Theory - Maze representation and pathfinding
• Object-Oriented Programming - Clean architecture with inheritance

📋 Prerequisites

Before running this project, make sure you have:

• C++ compiler with C++17 support (GCC 7+, Clang 5+, or MSVC 2017+)
• SFML 2.5 or higher
• CMake 3.10+ (recommended for building)

Installing SFML

Windows (vcpkg)

vcpkg install sfml

macOS (Homebrew)

brew install sfml

Ubuntu/Debian

sudo apt-get install libsfml-dev

🚀 Getting Started

Clone the Repository

git clone https://github.com/yourusername/pacman-ai-game.git
cd pacman-ai-game

Build the Project

Using CMake (Recommended)

mkdir build
cd build
cmake ..
make

Manual Compilation

g++ -std=c++17 -o pacman *.cpp -lsfml-graphics -lsfml-window -lsfml-system

Run the Game

./pacman

🎯 How to Play

• Arrow Keys or WASD - Move Pac-Man
• ESC - Pause/Exit game
• R - Restart game

Objective

• Collect all food items while avoiding the ghosts
• Each ghost has different behavior patterns:
  • Red Ghost (BFS): Direct pursuer with occasional random moves
  • Blue Ghost (DFS): Patrols between food items unpredictably
  • Green Ghost (Dijkstra): Uses optimal pathfinding considering food positions

🏗️ Project Structure

pacman-ai-game/
├── src/
│   ├── ghost.h                 # Base ghost class
│   ├── ghost.cpp              
│   ├── bfsGhost.h             # BFS pathfinding ghost
│   ├── bfsGhost.cpp           
│   ├── dfsGhost.h             # DFS pathfinding ghost
│   ├── dfsGhost.cpp           
│   ├── dijkstraGhost.h        # Dijkstra pathfinding ghost
│   ├── dijkstraGhost.cpp      
│   ├── pacman.h               # Player class
│   ├── pacman.cpp             
│   ├── Graph.h                # Graph representation and algorithms
│   ├── Graph.cpp              
│   ├── Food.h                 # Food item class
│   ├── Food.cpp               
│   └── main.cpp               # Main game loop
├── Assets/
│   ├── images/                # Sprite images
│   └── sounds/                # Audio files
├── CMakeLists.txt             # CMake build configuration
└── README.md                  # This file

🤖 AI Algorithm Details

Ghost Base Class

• Anti-camping system: Detects when ghosts are stuck and forces movement
• Polymorphic movement: Uses strategy pattern for different AI behaviors
• Shared movement logic: Common pathfinding execution and sprite animation

BFS Ghost (Breadth-First Search)

• Behavior: Direct pursuit of Pac-Man
• Features:
  • 20% chance of random movement for unpredictability
  • Guaranteed shortest path when chasing
  • More aggressive when few food items remain

A Ghost (A-Star Search)*

• Behavior: Intelligent pursuit of Pac-Man using heuristic-guided pathfinding
• Features:
  • 20% chance of random movement for unpredictability
  • Guaranteed optimal path with improved efficiency over BFS
  • Uses Manhattan distance heuristic to prioritize promising directions
  • More aggressive when few food items remain
  • Faster pathfinding compared to pure BFS while maintaining shortest path guarantee

Dijkstra Ghost (Dijkstra's Algorithm)

• Behavior: Optimal pathfinding with weighted edges
• Features:
  • Considers food positions when calculating paths
  • Uses weighted graph based on food density
  • Most sophisticated AI with strategic positioning

🔧 Customization

Adding New Ghost Types

1. Inherit from the ghost base class
2. Override the findPath() methods
3. Implement your pathfinding strategy
4. Add to the game's ghost vector

Example:

class MyCustomGhost : public ghost {
protected:
    std::vector<int> findPath(int ghostNodeId, int pacmanNodeId, Graph& g) override {
        // Your custom pathfinding logic here
        return customAlgorithm(ghostNodeId, pacmanNodeId, g);
    }
};

Modifying Game Parameters

• Ghost speed: Adjust speed member in ghost constructors
• Path recalculation frequency: Modify moveCounter threshold in movement()
• Anti-camping sensitivity: Change stationaryCounter threshold in isStuck()

🤝 Contributing

Contributions are welcome! Here are some ways you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

🙏 Acknowledgments

• Classic Pac-Man game by Namco for inspiration
• SFML community for excellent documentation
• Graph algorithm implementations adapted from computer science literature

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⭐ Star this repository if it helped you learn about game AI or graph algorithms!