https://github.com/apostolos-k/ntua-ai-notebooks

Jupyter notebooks for the Artificial Intelligence course's lab at Ece Ntua 2022-23.
https://github.com/apostolos-k/ntua-ai-notebooks

artificial-intelligence jupyter-notebook maze-generator ntua-ece prolog python recommender-system

Last synced: 5 days ago
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Jupyter notebooks for the Artificial Intelligence course's lab at Ece Ntua 2022-23.

• Host: GitHub
• URL: https://github.com/apostolos-k/ntua-ai-notebooks
• Owner: apostolos-k
• License: mit
• Created: 2023-06-16T17:47:25.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-08T11:21:51.000Z (3 months ago)
• Last Synced: 2024-08-08T13:31:12.182Z (3 months ago)
• Topics: artificial-intelligence, jupyter-notebook, maze-generator, ntua-ece, prolog, python, recommender-system
• Language: Jupyter Notebook
• Homepage:
• Size: 10.9 MB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Artificial Intelligence - ECE NTUA 2022-23

This repository contains Jupyter notebooks from the Artificial Intelligence course labs at ECE NTUA for the academic year 2022-23. The labs explore various AI techniques and algorithms, with practical implementations and simulations.

## Contributors

- [Karam Konstantinos](https://github.com/KostasKram)

- [Kolios Apostolos](https://github.com/apostolos-k)

## Labs Overview

### Lab 1 - Maze Generator & Pathfinding

In this lab, we implemented an N x N maze generator using the Randomized Depth-First Search algorithm (Iterative Implementation). The generated mazes are then used to test various pathfinding algorithms:

- **Dijkstra's Algorithm**

- **Best-First Search**

- **A\* Algorithm**

For the A\* Algorithm, we utilized different heuristic functions such as Manhattan and Euclidean distances to find the shortest path efficiently.

Additionally, we created a simulation of a chase inside the maze between a player and a ghost, implementing the Alpha-Beta pruning algorithm for the ghost's decision-making process.

### Lab 2 - Movie Recommender System in Prolog

In this lab, we developed a movie recommender system using Prolog, integrated within a Jupyter notebook via the `pyswip` library. The system offers two types of recommendations:

- **Content-Based Recommendations:** Suggests movies based on characteristics such as genre, year, actors, etc.

- **Collaborative Filtering:** Recommends movies based on user ratings of other movies, providing a similarity score to indicate how likely a movie is to be enjoyed by the user.

The recommendation system includes a scale to measure the likelihood of a user liking a particular movie, offering a more personalized suggestion.

## Reports

All lab reports and explanations are provided in Greek.