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Artificial Intelligence · Major Project
Chess Engine with Minimax & Alpha-Beta
Playable chess against an engine using search and evaluation
3-4 weeks build
Overview
A chess game you can play against your own engine. python-chess handles legal move generation, while the engine is written from scratch: minimax with alpha-beta pruning, iterative deepening, move ordering and a transposition table, guided by an evaluation built from material and piece-square tables. A Pygame board supports drag-and-drop moves, difficulty levels and PGN export.
What makes it stand up
- 01Minimax search with alpha-beta pruning written from scratch
- 02Iterative deepening with a per-move time limit
- 03Move ordering and a Zobrist-hashed transposition table
- 04Evaluation from material, piece-square tables and mobility
- 05Pygame board with difficulty levels and PGN export
Module breakdown
- 01
Board and rules
Legal moves, check and draws via python-chess.
- 02
Search
Alpha-beta, iterative deepening and quiescence search.
- 03
Evaluation
Weighted material, position and mobility terms.
- 04
Interface
Pygame board with drag-and-drop and move history.
- 05
Benchmarks
Nodes searched and time per depth, with and without pruning.
After this, you will be able to
- Explain how alpha-beta pruning skips branches safely
- Defend the weights in your evaluation function
- Play a live game and explain the engine's reasoning
Technology stack
- Python
- python-chess
- Pygame
- NumPy
- Flask
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support