This project is a major part of our studies [MSC Advanced Computer Science (Computational Intelligence)] at the University of Kent (United Kingdom).
Subject title: An application of Machine Learning to the Card Game Hearthstone.
It is about an AI (Machine Learning system) for card oriented game, applied to the game HearthStone (Blizzard company).
Contributors:
- Maigret Aurélien, original author of the project
- Zajda Florent
- Colin Julien
Supervisor:
- Marek Grzes
This is an AI project (Machine Learning system) for a card game, applied to the game HearthStone (Blizzard company). Hearthstone is a digital collectible card game (and free-to-play) that revolves around turn-based matches between two opponents. All played with cards that can summon creatures, weapons, spells, secrets, ...
We are interested by both artificial intelligence and the game HearthStone, that is why we would like to make our project of studies about a machine learning system, which will have the ultimate goal of learning decks by itself, and be able to advise the player on what to do against the predicted deck.
More information on docs/Project.md.
| What you have to do | Deadline |
|---|---|
| Project Registrations Open | 10am 18th January 2016 |
| Submitted a Project Acceptance Form | 4pm 29th January 2016 |
| Early deliverable | 2pm 27th June 2016 |
| Submission of the corpus | 2pm 25th August 2016 |
| Submission of the dissertation | 2pm 12th September 2016 |
Python 3 and NodeJS are required.
In the Application directory:
npm install -g electron-prebuilt
npm install
Run the application (Application directory):
electron .
In the Application directory:
pip3 install Pillow
python3 fetch_data.py
Python 2 is required.
pip2 install lxml
pip2 install requests
python2 scraping.py
