AI_Assignment

Assignment for Chess AI framework implementing Minimax and Alpha-Beta pruning search strategies.
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Features

• Minimax Search (minimax.py):

  • Pure minimax with optional move-ordering (captures first)
  • Randomized move exploration for diversity

• Alpha-Beta Pruning (alphabeta.py):

  • Standard alpha-beta search with cut-offs
  • Efficient branch pruning to accelerate deep searches

• Evaluation Module (evaluate.py):

  • evaluate_board(board) — full-board heuristic for positional strength
  • E(board) — streamlined evaluation for terminal/depth-limit positions

• Play Script (play_game.py):

  • Command-line interface to pit two AI agents (Minimax vs Alpha-Beta) or AI vs. human

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Installation

# 1. Clone this repository
git clone https://github.com/The0winner0/AI-Assignment.git
cd AI_Assignment

# 2. Create a virtual environment
python3 -m venv venv

# 3. Activate the environment
# On Linux/macOS:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# 4. Install dependencies
pip install -r requirements.txt

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Usage

Run the main play script:

python3 play_game.py

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Code Structure

AI_Assignment/
├── evaluate.py        # Board evaluation functions: evaluate_board, E
├── minimax.py         # `minimax(env, board, depth, is_maximizing, use_randomness)`
├── alphabeta.py       # `alphabeta(env, board, depth, alpha, beta, is_maximizing)`
├── play_game.py       # Main CLI for running matches
├── requirements.txt   # Python package dependencies
└── README.md          # Project overview and instructions

Anuj Sharma CS22B007

Jyothiraditya CS22B002

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