Development of an Artificial Intelligence (AI) in Python capable of playing ChessQuito, a strategic board game inspired by chess and available in three variants: Queen–Queen, Queen–Pawn, and Queen–King.
This project was completed as part of the Introduction to Artificial Intelligence module during the second year of the Computer Science Bachelor’s degree at the University of Lorraine.
The objective was to design an AI able to analyze the board, evaluate positions, and make optimal decisions depending on the selected variant.
The AI is based on a MinMax algorithm with alpha-beta pruning, combined with a dynamic weighted heuristic evaluation considering:
- Piece value and position
- Mobility and safety
- Threats, support, and board control
Three dedicated modules handle:
- The placement phase
- The playing phase
- The contextual evaluation depending on the chosen game mode
To fine-tune heuristic weights and search depth, numerous AI vs AI matches were conducted, enabling optimization of both responsiveness and strategic consistency across variants.
This project provided hands-on experience in:
- Algorithmic design and optimization
- Heuristic evaluation and strategic reasoning
- Game AI development and testing
- Collaborative programming (team of three)
University project completed as part of the Introduction to Artificial Intelligence course — Licence 2 Informatique, University of Lorraine.