aI-lab-glider/ai-course-games-tree-search

Lab 02 - adversarial search

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 🧠💻.

TODO:

Search for TODO text in the repository with CTRL+F and replace it with you code written according to it.

How To Submit Solutions

• 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

How To Run

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-search
• source adversarial-search/bin/activate

Then install required packages:

• pip install -r requirements.txt

After that configure main.py:

• specify game (see games folder)
• specify algorithm (see algorithm folder)

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.