A cloud simulation library based on cloudsim, it aims to provide all known cloud scheduling algorithms with the help from community developers, as well as automate various simulation scenarios.

This is a Java-based implementation of the Chinese Checkers game with a graphical user interface (GUI). The game allows you to play against an AI opponent using the Alpha-Beta Pruning algorithm. The objective of the game is to move all your marbles to the opposite point of the star-shaped game board. The first player to achieve this wins the game.

Github Repo for Automatic Chess Board for Arduino Nano.

Principles Of AI Lab Exercises

An Implementation of the Filler game with a min-max based AI written in the elm programming language

Invincible TicTacToe AI agent

A game of Chinese checkers where a user is able to play against an AI with multi levels of difficulty. The internal ai utilises the min-max algorithm

Reversi game with Min-Max algorithm with alpha-beta pruning

🤖📚 Comprehensive AI Learning Repo: Master DFS, BFS, DLS, IDS, UCS, Bidirectional Search, Greedy, A*, Mini-max, Alpha-beta Pruning, and Genetic Algorithm. Your go-to resource for AI course notes, lab codes, and exploration

This repository contains all the lab assignments done in the CSE422 course of BracU.

An implementation of a chess engine developed using JavaScript and the Minimax algorithm for decision-making.

Play classic Tic-Tac-Toe against an intelligent AI opponent in your terminal.

Tic Tac Toe Game engine built with Javascript

a tic tac toe project I made in my first semester of University

chess game using python, and django

Homework-2-KR-AI

A simple tic-tac-toe game (commonly known as x's and o's) with an AI player and 3 modes(easy, medium, and hard). The hard mode is implemented using the min-max game-playing algorithm.

It is a simple chess game which runs on a flask server. It is equipped with one built-in chess engine. It has an assistant which gives the user some introduction, instructions and credits.

This homework is about Implementing a smart agent to play Quoridor, using Min-max, a heuristic function, Transposition Table and forward pruning. This homework is a part of many projects in a Fundamentals to AI course @ FUM.