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shaileshmathivanan / repository
https-github.com-shaileshmathivanan-AI_ProblemSolving_RA2411026050223
Web-based Tic-Tac-Toe game with an unbeatable AI using Minimax and Alpha-Beta Pruning algorithms. Includes real-time performance comparison of both methods.
#adversarial-search#ai#alpha-beta-pruning#artificial-intelligence#flask#game-ai
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README preview
🎮 Tic-Tac-Toe AI
Minimax vs Alpha-Beta Pruning
🚀 Live Demo: https://tic-tac-toe-ai-sdvc.onrender.com
📌 AI Lab Assignment
Interactive Game AI – Tic-Tac-Toe System using Adversarial Search Algorithms
🧠 Problem Description
A gaming system requires an intelligent opponent capable of making optimal decisions in a Tic-Tac-Toe game.
Traditional rule-based approaches lack strategic depth. This project implements an AI-based solution using Minimax Algorithm and Alpha-Beta Pruning to ensure optimal gameplay.
The AI evaluates all possible moves and always selects the best one, making it unbeatable.
🌐 System Overview
This is a web-based Tic-Tac-Toe game where a human player competes against an AI.
The system also provides real-time comparison between two algorithms:
- ⏱ Execution Time
- 🌳 Nodes Explored
- ⚡ Performance Efficiency
🏗 System Architecture
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Game Engine (ai.py) Implements Minimax and Alpha-Beta algorithms
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Web Server (app.py) Flask backend handling routes and APIs
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Frontend (HTML, CSS, JavaScript) Interactive UI for gameplay
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Comparison Module Displays algorithm performance metrics
⚙️ Algorithms Used
🔹 Minimax Algorithm
- Explores all possible game states
- Assumes opponent plays optimally
- Selects best move
Evaluation Scores:
- Win → +10
- Draw → 0
- Loss → -10
🔹 Alpha-Beta Pruning
- Optimized version of Minimax
- Skips unnecessary branches