University Devils is a Founders Lab Thesis group looking to find a way for post-secondary institutions to increase the number of and diversity of incoming applications through the utilization of gaming and gaming approaches in the recruitment process while staying low-cost. This propelling question guided the group through their work. The team’s work primarily focused on recruitment efforts at Arizona State University, but the concept can be modified and applied at other post-secondary institutions. The initial research showed that Arizona State University’s recruitment focused on visiting the high schools of prospective students and providing campus tours to interested students. A proposed alternative solution to aid in recruitment efforts through the utilization of gaming was to create an online multiplayer game that prospective students could play from their own homes. The basic premise of the game is that one player is selected to be “the Professor” while the other players are part of “the Students.” To complete the game, the Students must complete a set of tasks while the Professor applies various obstacles to prevent the Students from winning. When a Student completes their objectives, they win and the game ends. The game was created using Unity. The group has completed a proof-of-concept of the proposed game and worked to advertise and market the game to students via social media. The team’s efforts have gained traction, and the group continues to work to gain traction and bring the idea to more prospective students.

This thesis is based on bringing together three different components: non-Euclidean geometric worlds, virtual reality, and environmental puzzles in video games. While all three exist in their own right in the world of video games, as well as combined in pairs, there are virtually no examples of all three together. Non-Euclidean environmental puzzle games have existed for around 10 years in various forms, short environmental puzzle games in virtual reality have come into existence in around the past five years, and non-Euclidean virtual reality exists mainly as non-video game short demos from the past few years. This project seeks to be able to bring these components together to create a proof of concept for how a game like this should function, particularly the integration of non-Euclidean virtual reality in the context of a video game. To do this, a Unity package which uses a custom system for creating worlds in a non-Euclidean way rather than Unity’s built-in components such as for transforms, collisions, and rendering was used. This was used in conjunction with the SteamVR implementation with Unity to create a cohesive and immersive player experience.

Machine learning has a near infinite number of applications, of which the potential has yet to have been fully harnessed and realized. This thesis will outline two departments that machine learning can be utilized in, and demonstrate the execution of one methodology in each department. The first department that will be described is self-play in video games, where a neural model will be researched and described that will teach a computer to complete a level of Super Mario World (1990) on its own. The neural model in question was inspired by the academic paper “Evolving Neural Networks through Augmenting Topologies”, which was written by Kenneth O. Stanley and Risto Miikkulainen of University of Texas at Austin. The model that will actually be described is from YouTuber SethBling of the California Institute of Technology. The second department that will be described is cybersecurity, where an algorithm is described from the academic paper “Process Based Volatile Memory Forensics for Ransomware Detection”, written by Asad Arfeen, Muhammad Asim Khan, Obad Zafar, and Usama Ahsan. This algorithm utilizes Python and the Volatility framework to detect malicious software in an infected system.