Adversarial search is an AI technique used to find the best move for a player in a two-player game 🤖🕹️. It involves exploring the game tree to determine the optimal move for one player while minimizing the opponent's chances of winning 👀🎲. Adversarial search algorithms, such as minimax and alpha-beta pruning, were commonly used for game playing 🎮. Recent developments in adversarial search actively use Monte Carlo algorithms combined with Reinforcement learning (e.g. Alpha Go) 🤖🤝. Overall, adversarial search is an important tool for developing intelligent systems that can make optimal decisions in competitive environments 🧠💻.
Search for TODO text in the repository with CTRL+F and replace it with you code written according to it.
- Clone repository: git clone:
git clone <repository url>
- Complete TODOS the exercises
- Commit your changes
git add <path to the changed files> git commit -m <commit message>
- Push changes to your repository main branch
git push -u origin master
The rest will be taken care of automatically. You can check the GRADE.md file for your grade / test results. Be aware that it may take some time (up to one hour) till this file
This class requires Python with version at least 3.8. Recommended way to run is to create a virtual environment first:
python -m venv adversarial-searchsource adversarial-search/bin/activate
Then install required packages:
pip install -r requirements.txt
After that configure main.py:
- specify
game(seegamesfolder) - specify algorithm (see
algorithmfolder)
and run the file.
Also, you can run a tournament between bots, by configuring file tournament.py.
In tournament a match is played between all configured bots for every configured game and results in the following output:
You will see a summary with total wins for every engaged bot and a table presenting statistics for bot vs bot clashes.