Minesweeper-AI

Minesweeper AI: CSP vs Probabilistic Reasoning

Overview

This project implements an intelligent agent to solve the Minesweeper game using two independent AI approaches:

• Constraint Satisfaction Problem (CSP)
• Probabilistic reasoning (exact inference + Monte Carlo)

The goal is not only to solve the game, but to compare deterministic vs uncertainty-based decision making in AI systems.

AI Approaches

1. CSP-based Agent

• Knowledge-based reasoning
• Logical inference using constraints (sentences)
• Identifies safe cells and mines with certainty

Strength:

• Guaranteed correctness when inference is possible

Limitation:

• Fails when uncertainty appears (no safe moves)

2. Probabilistic Agent

• Builds constraint models from the board

• Uses:

  • Exact inference (backtracking)
  • Monte Carlo sampling (for large states)

• Computes probability of each cell being a mine

Strength:

• Can act under uncertainty

Limitation:

• Slower and not always optimal

Comparison

Feature	CSP	Probabilistic
Deterministic	✅	❌
Handles uncertainty	❌	✅
Speed	Fast	Slower
Accuracy	High (when solvable)	Risk-based

System Architecture

Game Engine (Minesweeper)
        ↓
AI Agent
 ├── CSP Reasoning
 └── Probabilistic Inference
        ↓
Decision Making

Features

• Playable Minesweeper (Pygame)
• AI autoplay mode
• Safe vs mine visualization
• Performance stats (win rate, speed)
• Two independent AI agents

Installation & Run

Install dependencies

pip install pygame

Run CSP agent

python game/runner_csp.py

Run Probabilistic agent

python game/runner_probabilistic.py

Example Behavior

• CSP:

  • Plays perfectly when logic is sufficient
  • Stops when no safe move is known

• Probabilistic:

  • Chooses lowest-risk move
  • Continues even under uncertainty

Limitations

• No learning (non-adaptive)
• Performance drops on large boards
• Probabilistic agent depends on sampling

Future Improvements

• Hybrid AI (CSP + Probability)
• Reinforcement Learning agent
• Performance benchmarking system
• Visualization dashboard

Key Takeaways

• Difference between symbolic AI and probabilistic AI
• Trade-offs between certainty and risk
• Constraint modeling and inference techniques

Demo

• CSP

• Probability model

Author

Pham Tan Minh