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Tic-Tac-Toe based on AI. Some algorithms that I pretend to implement are Minimax, Alpha-Beta prudding and MCTS

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Intelligent_Tic-Tac-Toe

Adrián Carretero Canut

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Project Description

This project is about a Tic-Tac-Toe based on AI. After coursing the AI subject at university I decided to begin my first project working with Artificial Intelligence. I intend to implement three basic algorithms: Miniax, Alpha-Beta pruning and the Monte Carlo Tree Search (MCTS).

My first "milestone" is to create an AI with this 3 algorithms that plays a Tic-Tac-Toe game with a board size of 3x3. The goal is to end up with an AI capable of playing with boards of size 3x3, 4x4 or 5x5 (the player can choose which board wants to play with).

Dataset

  • There is no original dataset, although a future implementation could save games in an external .txt file and generate a rank (e.g., depending on the number of moves until the player beats the AI or based on the time spent on the game).

Conclusion

  • Once finished, the idea is to start a new project with this three algorithms, but instead of playing Tic Tac Toe, the idea would be to play Chess (which is way more complex than this project).

Future Work

  • Display a ranking.
  • Adding levels of complexity (Easy, Medium and Hard).
  • Offer an option for playing with a 4x4 board.
  • Offer an option for playing with a 5x5 board.
  • Export this AI to a Chess game.

Workflow

  • Once we execute the main.py file, the instructions on how to play are displayed.
  • It also displays the board and a message inviting the player to make a move.
  • Now is the AI's turn. After making a move, is turn for the player to make the move.

Organization

((here I would talk about how is the repository is organized, but as there are only 3 files, at this point is not worth it to spend time on this))

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Tic-Tac-Toe based on AI. Some algorithms that I pretend to implement are Minimax, Alpha-Beta prudding and MCTS

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