| Student Name | Student Number |
|---|---|
| Ana Ramos | up201904969 |
| Linda Rodrigues | up202005545 |
| Tomás Moreira | up202108858 |
- Heuristic Search Methods for Problem-Solving
- Adversarial Search Methods for Games
- Optimization Methods/Meta-Heuristics
Topic: Adversarial Search Methods for Two-Player Board Games
- Developing the AI for a 2-player game called Terrace by implementing the Minimax Algorithm (with alpha beta cuts).
- Game graphics using python's pygame library
- Game interface
- Game rules
- Menu
- Human-human, human-computer, and computer-computer game modes
- Different level of difficulty (easy, medium, hard), that differentiate in the depth limit for the Minimax algorithm and the heuristics used to evaluate the game state
- Algorithms:
- Minimax with alpha beta pruning, with different depth levels
Bonus Features:
- Music when the player wins
- Instructions and game legend
- Menu to allow the user to play again in the same mode
- Supervised Learning
Topic: Application of Machine Learning Models and Algorithms Related to Supervised Learning
- Conduct exploratory data analysis to understand class distribution and attribute values.
- Utilize various supervised learning algorithms (e.g., Decision Trees, Neural Networks, K-NN, SVM).
- Compare algorithm performance using evaluation metrics such as accuracy, precision, recall, and F1 measure.
- Evaluate models on both training and test sets.
Dataset: Autism Dataset for Toddlers