A comprehensive collection of AI practical programs and detailed study notes
Perfect for students, self-learners, and interview preparation
📖 Explore Docs · 🚀 Quick Start · 🎮 Play Tic-Tac-Toe · 🤝 Contribute
This repository contains well-structured Python implementations of classic AI algorithms alongside rich Markdown theory notes. Whether you're preparing for exams, learning AI fundamentals, or revising for interviews — this repo has you covered.
- 🐍 Clean Python Code – Readable, well-commented implementations
- 📚 Detailed Theory – Markdown notes with pseudocode and explanations
- 🎯 Exam-Ready – Summaries and key points for quick revision
- 🎮 Interactive Demo – Play Tic-Tac-Toe against Minimax AI
Artificial-Intelligence/
│
├── 📄 README.md # You are here
├── 📄 LICENSE # MIT License
├── 📄 CONTRIBUTING.md # Contribution guidelines
├── 📄 requirements.txt # Dependencies
├── 📄 .gitignore # Git ignore rules
│
├── 📘 01_AI_Introduction_Overview.md # Theory: Foundations of AI
├── 📘 05_LISP_and_PROLOG_Summary.md # Theory: LISP & PROLOG guide
├── 📘 AI_Practicals_02_to_08_Summary.md # Theory: All algorithms explained
│
├── 🐍 02_Depth_First_Search.py # DFS implementation
├── 🐍 03_Breadth_First_Search.py # BFS with goal & path finding
├── 🐍 04_Greedy_Best_First_Search.py # Heuristic-based search
├── 🐍 06_Minimax_Algorithm.py # Static tree Minimax
├── 🐍 07_TicTacToe_with_Minimax.py # Interactive Tic-Tac-Toe game
├── 🐍 08_Minimax_with_AlphaBeta_Pruning.py # Optimized Minimax
│
├── 📸 screenshots/ # Sample output screenshots
│ ├── 02_dfs_output.txt
│ ├── 03_bfs_output.txt
│ ├── 04_best_first_output.txt
│ ├── 06_minimax_output.txt
│ └── 08_alphabeta_output.txt
│
└── 🔧 .github/workflows/ # CI/CD automation
└── python-tests.yml
| # | Practical | Type | Description |
|---|---|---|---|
| 01 | AI Introduction | 📘 Theory | Foundations of AI, ML, DL, Turing Test, Learning Types |
| 02 | Depth First Search | 🐍 Code | Iterative DFS using stack on a graph |
| 03 | Breadth First Search | 🐍 Code | BFS with goal detection and path reconstruction |
| 04 | Greedy Best-First Search | 🐍 Code | Priority queue search using heuristics |
| 05 | LISP & PROLOG | 📘 Theory | Complete guide to AI programming languages |
| 06 | Minimax Algorithm | 🐍 Code | Decision-making on static game trees |
| 07 | Tic-Tac-Toe with Minimax | 🐍 Code | Play against an unbeatable AI! |
| 08 | Alpha-Beta Pruning | 🐍 Code | Optimized Minimax with pruning |
📘 Detailed theory, pseudocode, and explanations for Practicals 02–08 are in
AI_Practicals_02_to_08_Summary.md
- Python 3.x installed
# Clone the repository
git clone https://github.com/intronep666/Artificial-Intelligence.git
cd Artificial-Intelligence
# Run a practical (example: DFS)
python 02_Depth_First_Search.py
# Play Tic-Tac-Toe against AI
python 07_TicTacToe_with_Minimax.py | |
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| |
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| |
You are X. Enter position (1-9): _
Challenge the Minimax-powered AI — can you beat it? (Spoiler: You can't!) 😎
| Topic | Concepts Covered |
|---|---|
| Graph Search | DFS, BFS, Greedy Best-First, Heuristics |
| Game Theory | Minimax, Alpha-Beta Pruning, Zero-sum games |
| AI Foundations | Turing Test, AI vs ML vs DL, Learning paradigms |
| Data Structures | Stacks, Queues, Priority Queues, Trees |
| Field | Details |
|---|---|
| Name | PREXIT JOSHI |
| Roll Number | UE233118 |
| Branch | Computer Science and Engineering (CSE) |
| Institute | University Institute of Engineering and Technology, Panjab University (UIET, PU) |
| 📧 prexitjoshi@gmail.com | |
| GitHub | @intronep666 |
If you found this helpful, please consider giving a star ⭐ to the repository!
Contributions are welcome! Please read the Contributing Guidelines before submitting a PR.
This project is licensed under the MIT License – see the LICENSE file for details.
Made with ❤️ for AI enthusiasts
