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.
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.
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.
All lab reports and explanations are provided in Greek.