A PyTorch Toolbox for creating adversarial examples that fool neural networks.

Code for the paper "Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition"

Multiplayer snake AI

Automated Testing Framework for CARLA Simulator [ITSC 2022]

Halma game with an AI player, move validation, and dynamic board sizing

🤖 Chess AI using the minimax algorithm with alpha-beta pruning.

This is a AI bot for Chain Reaction game using minimax algorithm with alpha-beta pruning ang killer move heuristic.

This repository contains projects and exercises developed during the Artificial Intelligence course at UNAM. It covers topics such as fuzzy logic, adversarial search algorithms, intelligent agents, and more.

Solutions to Pacman AI Multi-Agent Search problems

Computer program that plays chess.

Artificial Intelligence + Deep Learning

Solutions to practical assignments of Artificial Intelligence course (CE-417) at Sharif University of Technology

An optimal Tic-Tac-Toe AI that uses the Minimax algorithm with alpha-beta pruning and depth-aware evaluation to select the best move for winning.

Monte Carlo Tree Search - A C++ MPI implementation

An AI Agent based on Alpha Beta Pruning for the Tic Tac Toe Game.

In this project, you will design agents for the classic version of Pacman, including ghosts. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.

Collection of Notebooks in Google Colab directly usable for study activities

A sophisticated AI-powered debate platform that integrates Large Language Models with Genetic Algorithms and Adversarial Search to create a dynamic and adaptive debating experience.

The phase 2 of my AI project, which is adversarial search in Pacman game for reaching the best utility and avoiding ghosts. Minimax with alpha-beta pruning and Expectimax is implemented.

This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"