1.Chess

Phase 1. Core Board & Piece Engine

Implement the foundational chess board, pieces, and movement logic ensuring internal consistency and rule correctness.

  • Represent an 8×8 board with piece objects.
  • Implement basic legal moves for all pieces.
  • Validate move boundaries and collisions.
  • Provide ASCII rendering for debugging.

Functional result : A fully functional internal board where each piece can execute all basic legal moves without turn rules, checks, or captures. The state updates correctly and can be inspected programmatically or via console rendering.

Phase 2. Game State Manager (Turns, Captures, Check Detection)

Build the core of the game state, handling turns, captures, and checks.

  • Enforce alternate turns.
  • Implement capture rules.
  • Detect check conditions.
  • Track history of moves.

Functional result : A turn-based playable engine allowing two players to move pieces legally, capture opponents, and trigger correct check detection. The game state consistently updates move history and enforces turn legality.

Phase 3. Advanced Rules (Checkmate, Stalemate, Special Moves)

Add full chess rule support, including endgame conditions and special moves.

  • Implement checkmate and stalemate logic.
  • Add castling, en passant, pawn promotion.
  • Validate special-move legality precisely.

Functional result : A complete rules engine correctly determining match endings and enabling all official chess mechanics, including castling, en passant, and promotions, ensuring any illegal special move is rejected.

Phase 4. Graphical Interface (Tkinter or Pygame)

Create an interactive visual chessboard.

  • Render board and pieces graphically.
  • Allow mouse-based selection and movement.
  • Highlight legal moves.
  • Display captures and turn indicator.

Functional result : A fully interactive GUI where players can click pieces, view valid moves, and complete turns visually. Moves update the board in real time, keeping consistent with the engine.

Phase 5. AI Opponent (Minimax + Heuristics)

Implement a challenging computer opponent using search algorithms.

  • Add minimax with alpha-beta pruning.
  • Create a position evaluation function.
  • Configure difficulty levels based on depth.

Functional result : A playable human-vs-AI mode where the computer generates legal, strategic moves using minimax evaluation. Difficulty levels adjust depth, creating distinct play experiences.

Phase 6. Persistence, PGN Support & Analysis Tools

Add advanced features for game storage, loading, and analysis.

  • Save/load games in PGN-like format.
  • Provide move navigation and replay.
  • Implement position evaluation visualizations (text-based).

Functional result : A system capable of saving and loading matches, replaying previous moves, and offering simple analysis indicators. Players can resume games and inspect historical positions reliably.