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.
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- All Subjects: artificial intelligence
- All Subjects: conservation
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.
ContributorsToye, Abigail Elizabeth (Author) / Linde, Jennifer (Thesis director) / Abele, Kelsey (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The Mexican gray wolf (Canis lupus baileyi) is a genetically distinct subspecies of the gray wolf (Canis lupus) that was driven to the brink of extinction as a result of human persecution. The wolf is listed as Endangered under the Endangered Species Act, and a recovery program is underway in Arizona and New Mexico to restore its population. However, the wolf is struggling to recover due to high mortality, which is a result of continued human hostility toward it. This thesis examines historical and current human attitudes toward the wolf and the implications that they have had on the extermination and recovery of the subspecies. An overview is given of wolf biology, the history of wolf extermination and recovery, and recent events relating to the recovery of the wolf. Negative impacts on ranching, hunting, and human safety are the main reasons for opposition toward wolves and wolf recovery; these concerns are analyzed, and solutions to them are proposed, with the goal of addressing them while fostering non-lethal coexistence with the wolf. In addition, opposition to wolves and wolf recovery is tied in with larger socio-political issues and is influenced by the representation of the wolf in culture; these issues in the context of wolves are also analyzed.
ContributorsLenk, Heather Nicole (Author) / Smith, Andrew (Thesis director) / Minteer, Ben (Committee member) / Brown, David E. (Committee member) / School of Life Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
A literature review summarizing the current status of conservation efforts of the Mojave Desert tortoise (Gopherus agassizii) including a brief overview of the Endangered Species Act (ESA) and its applicability to this species' conservation. A genetic and physiological comparison of the morphologically similar Mojave species with the Sonoran (Gopherus morafkai) species proceeded by an analysis of if and how the ESA should apply to the Sonoran population. Analysis of current plans and interagency cooperations followed by a multi-step proposal on how best to conserve the Sonoran population of Desert tortoise.
ContributorsKulik, Elise Chikako (Author) / Kusumi, Kenro (Thesis director) / Tollis, Marc (Committee member) / Wilson Sayres, Melissa (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
The objective of this research is to determine an approach for automating the learning of the initial lexicon used in translating natural language sentences to their formal knowledge representations based on lambda-calculus expressions. Using a universal knowledge representation and its associated parser, this research attempts to use word alignment techniques to align natural language sentences to the linearized parses of their associated knowledge representations in order to learn the meanings of individual words. The work includes proposing and analyzing an approach that can be used to learn some of the initial lexicon.
ContributorsBaldwin, Amy Lynn (Author) / Baral, Chitta (Thesis director) / Vo, Nguyen (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Artificial intelligence (AI) is a burgeoning technology, industry, and field of study. While interest levels regarding its applications in marketing have not yet translated into widespread adoption, AI holds tremendous potential for vastly altering how marketing is done. As such, AI in marketing is a crucial topic to research. By analyzing its current applications, its potential use cases in the near future, how to implement it and its areas for improvement, we can achieve a high-level understanding of AI's long-term implications in marketing. AI offers an improvement to current marketing tactics, as well as entirely new ways of creating and distributing value to customers. For example, programmatic advertising and social media marketing can allow for a more comprehensive view of customer behavior, predictive analytics, and deeper insights through integration with AI. New marketing tools like biometrics, voice, and conversational user interfaces offer novel ways to add value for brands and consumers alike. These innovations all carry similar characteristics of hyper-personalization, efficient spending, scalable experiences, and deep insights. There are important issues that need to be addressed before AI is extensively implemented, including the potential for it to be used maliciously, its effects on job displacement, and the technology itself. The recent progression of AI in marketing is indicative that it will be adopted by a majority of companies soon. The long-term implications of vast implementation are crucial to consider, as an AI-powered industry entails fundamental changes to the skill-sets required to thrive, the way marketers and brands work, and consumer expectations.
ContributorsCannella, James (Author) / Ostrom, Amy (Thesis director) / Giles, Charles (Committee member) / Department of Marketing (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks. For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task. This research project aims to investigate human-robot trust through a collaborative game of logic that can be played with a human and a robot together. This thesis details the development of a game of logic that could be used for this purpose. The game of logic is based upon a popular game in AI research called ‘Wumpus World’. The original Wumpus World game was a low-interactivity game to be played by humans alone. In this project, the Wumpus World game is modified for a high degree of interactivity with a human player, while also allowing the game to be played simultaneously by an AI algorithm.
ContributorsBoateng, Andrew Owusu (Author) / Sodemann, Angela (Thesis director) / Martin, Thomas (Committee member) / Software Engineering (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
White-nose syndrome (WNS) is a fungal infection devastating bat populations throughout eastern North America. WNS is caused by a fungus, Pseudogymnoascus destructans (Pd), that invades the skin of hibernating bats. While there are a number of treatments being researched, there is currently no effective treatment for WNS that is deployed in the field, except a few being tested on a limited scale. Bats have lowered immune function and response during hibernation, which may increase susceptibility to infection during the winter months. Antimicrobial peptides (AMPs) are a crucial component of the innate immune system and serve as barriers against infection. AMPs are constitutively expressed on skin and facilitate wound healing, stimulate other immune responses, and may also stay active on bat skin during hibernation. AMPs are expressed by all tissues, have direct killing abilities against microbes, and are a potential treatment for bats infected with Pd. In this investigation, the fungicidal activity of several readily available commercial AMPs were compared, and killing assay protocols previously investigated by Frasier and Lake were replicated to establish a control trial for use in future killing assays. Another aim of this investigation was to synthesize a bat-derived AMP for use in the killing assay. Sequences of bat-derived AMPs have been identified in bat skin samples obtained from a large geographic sampling of susceptible and resistant species. Contact was made with GenScript Inc., the company from which commercially available AMPs were purchased, to determine the characteristics of peptide sequences needed to synthesize an AMP for lab use. Based on recommendations from GenScript Inc., peptide sequences need to have a hydrophobicity of less than 50% and a sequence length of less than 50 amino acids. These criteria serve as a potential barrier because none of the known bat-derived sequences analyzed satisfy both of these requirements. The final aim of this study was to generate a conceptual model of the immune response molecules activated when bats are exposed to a fungal pathogen such as Pd. Overall, this work investigated sources of variability between trials of the killing assay, analyzed known bat-derived peptide sequences, and generated a conceptual model that will serve as a guideline for identification of immune response molecules on the skin of bats in future proteomics work.
ContributorsBarton, Madisen L (Author) / Moore, Marianne (Thesis director) / Penton, Christopher (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The International Union for Conservation of Nature's Red List of Threatened Species is the most comprehensive and objective global approach to evaluate the conservation status of species by categorizing species based on relative extinction risk. For the Global Muranidae IUCN Red List assessment, all known, taxonomically valid species of Muraenidae were assessed for their extinction risk using the IUCN Red List Global Categories and Criteria. Of all 208 Muraenidae species, it was concluded that 86% of species qualified for Least Concern, 13% of species are Data Deficient, and 1% of species qualified for a threatened category. Channomuraena bauchotae is listed as threatened under VU D2 and Gymnothorax parini qualified for VU B2ab(iii). This study will have brought the International Union for the Conservation of Nature one step closer to their goal of conducting Red List assessments of all the world's species(not including microorganisms). Future implications of this study may include future monitoring of key habitat areas and species or conducting further research to gain a more in depth understanding of the life history and threats to Muraenidae.
ContributorsLaurence, Paige Marie (Author) / Polidoro, Beth (Thesis director) / Ralph, Gina (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05