Project Title: AI Opponent for Chess Game using C++

Project Summary
This project involves creating a Chess Game where the player can compete against an AI opponent. The AI opponent is programmed using C++, utilizing algorithms like Minimax and Alpha-Beta Pruning for decision-making, providing a challenging chess experience. The game also includes a graphical user interface (GUI) and can be played locally.

Core Components

Chess Game Logic

• Representation of the chessboard and pieces using arrays or classes (e.g., 8x8 grid, piece objects).
• Legal move generation for each piece type, enforcing the rules of chess.

AI Opponent

• Minimax Algorithm: A decision-making algorithm that evaluates the best move by simulating all possible moves and their outcomes.
• Alpha-Beta Pruning: An optimization for Minimax that reduces the number of nodes evaluated in the search tree, improving performance.

Move Evaluation

• Evaluation function that scores positions based on material, piece positions, and other strategic factors.
• Heuristic functions to prioritize moves that lead to advantageous positions (e.g., controlling the center, piece activity).

Game Flow and Player Interaction

• A turn-based system where the player alternates with the AI.
• Player input is captured via keyboard/mouse to select pieces and make moves.

GUI (Graphical User Interface)

• Chessboard and pieces are drawn using libraries like SFML, SDL, or Qt.
• Displays move history, captured pieces, and check/checkmate status.

Move History and Undo

• Keeps track of all moves for the current game session.
• Allows players to undo moves during gameplay.

Endgame Detection

• Checks for checkmate, stalemate, or draw conditions after each move.
• Alerts the player when the game is over.

Technologies Used

• C++
• Chess-specific libraries (optional, e.g., for move generation)
• SFML/SDL/Qt (for GUI rendering)
• Standard C++ libraries (STL for containers, vectors, etc.)

Learning Outcomes

• Deep understanding of game tree search algorithms (Minimax, Alpha-Beta Pruning)
• Experience in implementing a complex board game logic and AI
• Familiarity with chess strategy and move evaluation
• Skill in creating a basic GUI for interactive games
• Optimization techniques for AI performance

Possible Enhancements

• Implement different AI difficulty levels by adjusting the depth of the Minimax search or the evaluation function.
• Add features like hint systems, move suggestions, or an opening book for the AI.
• Include multiplayer functionality (local or online) for player vs player.
• Add a more advanced AI algorithm, such as Monte Carlo Tree Search (MCTS), for even stronger opponents.
• Create a more sophisticated GUI with animations for piece movements and checkmate sequences.