Vintage Pacman Game developed in Flutter

Solutions of 1 and 2 Pacman projects of Berkeley AI course

Solution to some Pacman projects of Berkeley AI course

Team Project for Berkeley's Pacman Capture The Flag Competition

Principles & Applications of Artificial Intelligence at Amirkabir University of Technology course projects

Reinforcement Learning Course Project - IIT Bombay Fall 2018

multi-agent moving target pursuit in an unknown 4-connected grid world with persistent coverage planning

Pacman and Ms. Pacman reverse engineered in JavaScript

Pacman using c++ and Computer Graphics.

Explore foundational AI concepts through the Pac-Man projects, designed for UC Berkeley's CS 188 course. Implement search algorithms, multi-agent strategies, and reinforcement learning techniques in Python, emphasizing real-world applications. Engage in the Eutopia Pac-Man contest for a multiplayer capture-the-flag challenge

Berkeley Pacman Projects (1 and 2), Depth First Search, Breadth First Search, Uniform Cost Search ,A* Search , Heuristic Functions ,Suboptimal Search, Minimax Algorithm, Alpha-Beta Pruning, Expectimax, Constraint Santisfaction Problems, RLFA CSP problem, Propositional Logic, First-order Logic

A Monte-Carlo Tree Search mathod that enables two agents interact and work together in the game of Pacman Capture the Flag.

👻 UC Berkeley CS188 Intro to AI -- The Pac-Man Projects

Berkeley Pac-Man 🤤◽◽◽👻 projects 0, 1 & 2 solutions

C'est un projet PacMan

A simple PACMAN game developed in Borland C Graphics lib. (code copied from web)

University of Oulu A.I. coursework, Pacman projects. Original materials are offered by UC Berkeley.

I have completed four Pacman projects of the UC Berkeley CS188 Intro to Artificial Intelligence course. The Pac-Man projects were developed for CS 188. They apply an array of AI techniques to playing Pac-Man. They teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning.

UC Berkeley Pacman AI Projects

Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment.