This repository contains practical implementations for the Artificial Intelligence component of the Laboratory Practice II (310258) course offered in the Third Year of Computer Engineering (2019 Course) at Savitribai Phule Pune University. Explore the world of intelligent agents, search algorithms, and AI applications!
🏛️ Course Information:
- University: Savitribai Phule Pune University
- Course Name: Laboratory Practice II (310258)
- Focus: Artificial Intelligence
- Companion Courses:
- Artificial Intelligence (310253)
- Elective II (310254)
- Credit: 02
- Examination Scheme:
- Practical: 04 Hours/Week
- Term Work: 50 Marks
- Practical Exam: 25 Marks
🎯 Learning Objectives:
- Gain a deep understanding of search strategies used in AI, including informed, uninformed, and heuristic approaches.
- Apply fundamental AI principles to design solutions for problems involving:
- Problem-solving
- Inference
- Perception
- Knowledge representation
- Machine learning
- Design, develop, and implement interactive AI applications.
💡 Course Outcomes (Artificial Intelligence):
Upon completion of this laboratory component, students will be able to:
- CO1: Design intelligent systems leveraging different informed/uninformed search and heuristic algorithms.
- CO2: Apply core AI principles in crafting solutions that require problem-solving, inference, perception, knowledge representation, and learning capabilities.
- CO3: Design and develop interactive AI applications.
📂 Practical Implementations:
| Practical No. | Description |
|---|---|
| 1 | Implement Depth-First Search (DFS) and Breadth-First Search (BFS) algorithms. Use an undirected graph and develop a recursive algorithm to search all vertices within a graph or tree data structure. |
| 2 | Implement the A* Search algorithm to solve a game search problem. |
| 3 | Implement the Greedy Search algorithm for one of the following applications: |
| I) Selection Sort | |
| II) Minimum Spanning Tree | |
| III) Single-Source Shortest Path Problem | |
| IV) Job Scheduling Problem | |
| V) Prim's Minimal Spanning Tree Algorithm | |
| VI) Kruskal's Minimal Spanning Tree Algorithm | |
| VII) Dijkstra's Minimal Spanning Tree Algorithm | |
| 4 | Implement a solution for a Constraint Satisfaction Problem using Branch and Bound and Backtracking techniques for the N-Queens problem or a graph coloring problem. |
| 5 | Develop a basic chatbot for a suitable customer interaction application. |
| 6 | Implement one of the following Expert Systems: |
| I) Information Management System | |
| II) Hospital and Medical Facility System | |
| III) Help Desk Management System | |
| IV) Employee Performance Evaluation System | |
| V) Stock Market Trading System | |
| VI) Airline Scheduling and Cargo Scheduling System |
🚀 Getting Started:
Navigate to the directory of the AI practical implementation you want to explore. Each directory contains well-documented code files with clear instructions to guide your learning.
🙌 Contributions:
We encourage contributions, enhancements, and feedback from the AI and programming communities! If you have improvements, bug fixes, or additional practical examples to share, please open a pull request. Refer to our CONTRIBUTING.md file for guidelines.
📄 License:
This repository is distributed under the MIT License, allowing you to use, modify, and distribute the code for educational and personal projects.
Let's learn and build intelligent systems together! 🚀