A repository of multiple projects and labs done in the Udacity Artificial Intelligence Nanodegree (aind).
In this project, you will be writing code to implement two extensions of our sudoku solver. The first one will be to implement the technique called "naked twins". The second one will be to modify our existing code to solve a diagonal sudoku. To complete this project you will use the tools you learned about in the lesson, and build upon them.
Your goals are to implement the naked twins function, and write an AI agent that will solve the Diagonal Sudoku game.
In this project, students will develop an adversarial search agent to play the game "Isolation". Isolation is a deterministic, two-player game of perfect information in which the players alternate turns moving a single piece from one cell to another on a board. Whenever either player occupies a cell, that cell becomes blocked for the remainder of the game. The first player with no remaining legal moves loses, and the opponent is declared the winner. These rules are implemented in the isolation.Board class provided in the repository.
In this lab, you will teach Pac-Man to search his world to complete the following tasks:
find a single obstacle. find multiple obstacles. find the fastest way to eat all the food in the map.
In this project, you will define a group of problems in classical PDDL (Planning Domain Definition Language) for the air cargo domain discussed in the lectures. You will then set up the problems for search, experiment with various automatically generated heuristics, including planning graph heuristics, to solve the problems, and then provide an analysis of the results. Additionally, you will write a short research review paper on the historical development of planning techniques and their use in artificial intelligence.
In this project, you will build a system that can recognize words communicated using the American Sign Language (ASL). You will be provided a preprocessed dataset of tracked hand and nose positions extracted from video. Your goal would be to train a set of Hidden Markov Models (HMMs) using part of this dataset to try and identify individual words from test sequences.