Noah Parker

Personal

I am Noah Parker, a graduate of Rochester Institute of Technology with Bachelor of Science in Game Design and Development and a Minor in Psychology. I personal enjoy programming and learning about different forms of AI. I consider myself a hard worker that tries their best to complete any task given. I plan so that I can stay on top of my work. I am a quiet thinker that says what is on my mind and makes sure my point gets across.

So far, I know the following programming languages: C#, C++, Java, HTML, CSS, JavaScript, and Lua.

Some awards that I have receive are:

• Eagle Scout Award, from the Boy Scouts of America on July 2nd 2014
• Dean's List, from Rochester Institute of Technology from Fall of 2014 till Spring of 2016
• Black Belt, from The Academy of Modern Martial Arts in May of 2013

I come from Portland, Oregon. I went to Jesuit High School in Portland. I love the North West .

In School

During my college career, I worked on many projects from video games to data base management. Algorithms, 2D art, 3D modelling, graphic engine programming, version control, web development, and programming library creation are just some of the things that I have learned about.

Overview

A world of technology and humanity. Heists, Crime Families, Dictator AIs, and more await you here. The year is 2175, and American Citizens have elected an advanced Artificial Intelligence to run the country when they decided they had enough of flawed human electives. Everything is on the network and B.I.N.F. sees it all.

In this system, you make characters that live and act in this world that is driven entirely by your creativity and imagination. Steal invaluable data, become the head a of a crime family, or try to take down B.I.N.F., the AI dictator of America. Anything you can imagine you can do; with a bit of skill of course.

Use a unique "reverse dice" system to overcome unique challenges and reap the rewards you so deserve.

Overview

Build your community, take care of your family, avert disasters, and don’t let any of your neighbors go destitute. Any or all can win—or if you are not careful, all can lose.

In medieval North Africa, towns are built by families like yours. Show your strategic and collaborative skills in Lost & Found, a historical strategy game of balance between community and family needs.

Take on one of five roles in a struggle to address dilemmas, overcome crises, and avert disasters that threaten to destroy everything you know and love.

Look out for your family, but be sure to collaborate with your neighbors as well. Any number of players can win, but if anyone goes destitute, everyone loses.

Overview

When the player puts on the virtual reality headset they take over their cyber-persona: The User. The User wants to go into high ranking and secure systems and grab the most important part of the system: the soul.exe and get out. But this isn’t a simple task, once you are in you need to be strategic about what you are doing because the system want you out. However you are not alone in your task, you are able execute programs that will help defend you and help you reach the exit.

This turn-based, team strategy game put the player in the action as they are moving through the system, moving their motion controllers to summon a program minion or attack the enemy directly. Can you get in and get out without dying?

Overview

A 4-player, PvP brawler, where you take the role of a Beta Ranger as you try to fight your fellow rangers. Fighting over the only battery, you along with the other rangers must try to charge your piece of the mech to win. Each ranger has their own powers and skills that they can use to fight the other rangers.

This game was a 5 week project, made in Unity. It was co-created by Danielle Carmi, Joshua Malmquist, Robert Santiago, and myself.

Overview

Pulp Legends is a 2D couch co-op, side scrolling, platformer set in the Noir/Pulp Fiction Universe. You play as Detectives with odd powers who must work together in order to solve the murder of the police chief. Traverse dark alleys, abandoned factories and the seedy underground of New Pulp and bring justice to the city once again.

This game is a 5 week project, made in Unity. It was co-created by Anna Karwacki, Joshua Malmquist, Jason Noone, and myself.

This project has thought me a lot more about teamwork and organization. The team stayed in constant communication and made sure our work got done. This is currently the biggest video game that I have worked on.

Overview

It the survival of the weirdest. It is a board based around the idea of evolution, adaptations, and mutations.

This game is a three to eight player evolution-themed board game that focuses on using creature abilities, movement, attacking, and defending cause mutations in the player's creature, The mutations gives the players the chance to obtain habitat tokens; once a player gets four habitate tokens and reach the end zone they win the game.

Overview

For the AI of this project, several different algorithms that could be used for Chess were studied and implemented. They were programmed in a way that allowed them to figure out the optimal move. There were several different types of AI that were used for this project.

Optimal Decision Making w/ Uncertainty AI

This AI focused on the idea of Optimal Decision Making. Through the use of many variables, it figures out the optimal move to take next.

Optimal Decision Making has three main parts:

1. Predicting the outcome of a decision
2. Assigns a value to that decision
3. Finds the decision that maximizes the value

This allowed the AI to makes judgements of each possible move then do the one that gives itself the greatest advantage over the opponent.

This AI is the smartest AI that has been created.

Maximin/ Minimax AI using Alpha-Beta Pruning

This AI implemented the minimax algorithm which made use of Alpha-Beta pruning and Zero-sum pruning. This makes this AI a depth-first sorting algorithm.

A Maximin algorithm looks to minimize the worst possible scenario that could occur from the next player's move. This is done by looking at all possible moves that can be made. Then it looks at all possible moves that the other player could do off that move, this will determine the worst possible combinations of moves. Whichever move returns to highest value, is the move that the AI will make.

This process takes a long time, especially if the AI is looking several moves ahead. Pruning is used to cut off nonviable paths.

• Alpha-Beta Pruning is a search algorithm that stops completely evaluating an action when at least one possibility has been found that proves the action to be worse than a previously examined action.
• Zero-sum Pruning uses the idea that the worst action for player 1 is the best action for player 2. It seeks to maximize player 1's action.

Because of depth-first searching, the main problem is the large amount of time that it takes determine the best possible move. Pruning helps cut this time dramatically.

Supervised Machine Learning using a Neural Network

This AI parses a large database of Chess games through a neural network to create a complex web of weights and nodes. Parsing is done by inputting every move in a game and then seeing what the outcome is. That outcome is then compared to the outcome of the actual game, which leads to backwards propagation. Weights are adjusted by backwards propagation to make the AI ‘learn’ from the game.

THIS HAS NOT BEEN IMPLEMENTED FULLY YET.

Unsupervised Machine Learning using a Neural Network

This AI focuses on ‘learning’ from playing many games of chess over time. Initially, the AI will have a neural network with randomly weighted nodes. As it plays, it will parse moves through the networks to determine which ones it things are good. Once the other player makes a move, it will backward propagate the result of their move through the network to adjust the weights.

THIS HAS NOT BEEN IMPLEMENTED FULLY YET.

Three Stage Chess AI

This AI is based off of the idea that in a game of Chess, there are three phases to it: the opening, the mid-game, and the end game. This AI gets separated into those three phases and does different things depending on the phase it is in.

• Opening: The phase where the AI develops its' pieces, gets their king to safety, and tries to control the center of the board.
• Mid-Game: The phase where the AI attacks with and defends its' pieces.
• End Game: The phase where most pieces are off the board and the king is usually checked.

THIS HAS NOT BEEN IMPLEMENTED FULLY YET.

Variable Modifier AI

This AI is the same as the Optimal Decision Making AI, but it allows the user to change the values of the variables that the AI uses.

THIS HAS NOT BEEN IMPLEMENTED FULLY YET.

Random Choice AI

This AI is used as a control to test other AIs against. It looks at all possible moves that can be made, then chooses one at random. It is meant to show that the other AIs are smarter than random.

Overview

Depth Redemption a rogue-like game originally created in Java then ported over to Javascript with the help of Phaser.

The game was first created during a 24 hour hackathon by Peter Cowal and myself. After the hackathon, I decided to take it upon myself and add more to it. I implemented several features: version history, more items, more enemies, added more to the main menu, and I modified the dungeon generator. It also contains an inventory system, leveling up, difficult levels, randomly generated dungeon floors, pre-made dungeon floors, fog of war, weapon range, and text output.

Depth Redemption was the first personal project that I felt very passionate about continuing after others stopped working on it. That is why I moved it over to Javascript, so that I could continue to work on it in a language that allows for many people to play it.