Principles Of AI Lab Exercises

Invincible TicTacToe AI agent

Tic-tac-toe game in python, using pygame. (with AI adversary)

Homework-2-KR-AI

Development of an Artificial Intelligence (AI) in Python capable of playing ChessQuito using a min-max (alpha-beta) algorithm.

chess game using python, and django

Python package for Visual Studio Code extension dec-tree-vscode for Decision Tree

Virus MIN-MAX: A Strategy and Contamination Game This game challenges your strategy and your ability to control territory. You play against an AI (based on the MIN-MAX algorithm). The goal of the game is to convert as many opponent pieces as possible to your own color by contaminating them.

Python projects for Introduction to Artificial Intelligence course at Warsaw University of Technology.

Customer Churn Prediction Model & Data Pipeline

The Tic Tac Toe game project is a classic implementation of the popular game, developed in Python. It offers two exciting modes of play: single-player and multiplayer. The game is played on a 3x3 grid, where players take turns marking their moves with 'X' and 'O' symbols.

Projet réalisé avec @guillaumeGRANDY dans le cadre du module d'Intelligence Artificielle/Apprentissage par renforcement en 4ème année à Polytech Lyon. Le but du projet était d'implémenter des algorithmes tels que min-max, alpha-beta et MCTS afin de jouer au morpion

An AI-powered strategic board game was developed using Min-Max Algorithm, enabling intelligent decision-making during gameplay.

Artificial Intelligence exercises.

Tic Tac Toe AI using Minimax algorithm in Python

A console chess game in python working on the Artificial Intelligence min-max algorithm

A simple Tic Tac Toe game in Python that supports both single-player and two-player modes. The game runs in the command prompt, providing a classic Tic Tac Toe terminal-based interface.

Minimax algorithm and alpha-beta pruning are applied to solve competing vacuum cleaners that want to clean a room from dirts.

This project provides game logic for AI and human players using the Minimax algorithm with alpha-beta pruning. It includes functions for managing game states, simulating AI vs AI, AI vs Human, and AI vs Monte Carlo gameplay, and visualizing the board.

Tic-tac-toe environment for machine learning algorithms like min-max algorithm or qlearning with gym environment compatibility.