Principles Of AI Lab Exercises
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Updated
Jan 13, 2024 - Python
Principles Of AI Lab Exercises
Invincible TicTacToe AI agent
Homework-2-KR-AI
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 is a modified version of the classic checkers game created for academic purposes as a project for the AI Laboratory. The game introduces unique features, including vertical piece movements , special power pieces such as archers, king, and hero. The AI is implemented using the Min-Max algorithm with a depth level of 3 for strategic gameplay.
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.
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.
Artificial Intelligence exercises.
Python projects for Introduction to Artificial Intelligence course at Warsaw University of Technology.
A console chess game in python working on the Artificial Intelligence min-max algorithm
TicTacToe with AI [Beginner's Project]
This repo is all about implementing tic-tac-toe ai built using min-max algorithm.
Tic-tac-toe environment for machine learning algorithms like min-max algorithm or qlearning with gym environment compatibility.
A repository with some reinforcement learning projects
implement an AI agent for playing a two-player game. Assignment 2 of the course COL-333: Introduction to Artificial Intelligence Semester I, 2022-23 at IIT Delhi.
backend engine for games
This codebase features AI algorithms for smarter gaming experiences. It includes implementations of strategies like Greedy, Minimax, Alpha-Beta Pruning, and Expectimax. These algorithms empower AI agents to make optimal decisions in games, enhancing strategic gameplay.
Minimax algorithm and alpha-beta pruning are applied to solve competing vacuum cleaners that want to clean a room from dirts.
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