Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating environments, using photogrammetry, or other means. Geological formations and the surrounding terrain can be created using noise based algorithms such as Perlin noise. However, interpreting noise in this manner has a number of challenges due to the pseudo-random nature of noise. We will discuss how to generate noise, how to render noise, and the challenges in interpreting noise.
The goal of this thesis project was to develop a digital, quantitative assessment of executive functioning skills and problem solving abilities. This assessment was intended to serve as a relative measure of executive functions and problem solving abilities rather than a diagnosis; the main purpose was to identify areas for improvement and provide individuals with an understanding of their current ability levels. To achieve this goal, we developed a web-based assessment through Unity that used gamelike modifications of Flanker, Antisaccade, Embedded Images, Raven’s Matrices, and Color / Order Memory tasks. Participants were invited to access the assessment at www.ExecutiveFunctionLevel.com to complete the assessment and their results were analyzed. The findings of this project indicate that these tasks accurately represent executive functioning skills, the Flanker Effect is present in the collected data, and there is a notable correlation between each of the REFLEX challenges. In conclusion, we successfully developed a short, gamelike, online assessment of executive functioning and problem solving abilities. Future developments of REFLEX could look into immediate scoring, developing a mobile application, and externally validating the results.
The goal of this thesis project was to develop a digital, quantitative assessment of executive functioning skills and problem solving abilities. This assessment was intended to serve as a relative measure of executive functions and problem solving abilities rather than a diagnosis; the main purpose was to identify areas for improvement and provide individuals with an understanding of their current ability levels. To achieve this goal, we developed a web-based assessment through Unity that used gamelike modifications of Flanker, Antisaccade, Embedded Images, Raven’s Matrices, and Color / Order Memory tasks. Participants were invited to access the assessment at www.ExecutiveFunctionLevel.com to complete the assessment and their results were analyzed. The findings of this project indicate that these tasks accurately represent executive functioning skills, the Flanker Effect is present in the collected data, and there is a notable correlation between each of the REFLEX challenges. In conclusion, we successfully developed a short, gamelike, online assessment of executive functioning and problem solving abilities. Future developments of REFLEX could look into immediate scoring, developing a mobile application, and externally validating the results.
Designing these agents to cover every case of human interaction is difficult, and usually
imperfect, as human players are capable of learning to overcome these agents in unintended
ways. Artificial intelligence is a growing field that seeks to solve problems by simulating
learning in specific environments. The aim of this paper is to explore the applications that the
self play learning branch of artificial intelligence may pose on game development in the future,
and to attempt to implement a working version of a self play agent learning to play a Pokemon
battle. Originally designed Pokemon battle behavior is often suboptimal, getting stuck making
ineffective or incorrect choices, so training a self play model to learn the strategy and structure of
Pokemon battles from a clean slate would result in an organic agent that would outperform the
original behavior of the computer controlled agents. Though unsuccessful in my implementation,
this paper serves as a record of the exploration of this field, and a log of what worked and what
did not, in order to benefit any future person interested in the same topics.