Matching Items (14)
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- All Subjects: artificial intelligence
- All Subjects: sports
- Member of: Theses and Dissertations
- Resource Type: Text
Description
Historical trends of artificial intelligence have, as shown by recent quantitative and qualitative studies, shown that the reported threats (as understood by the general public) are vastly different from the tech industry’s most pressing and vital concerns. The modern AI that most people interact with on a daily basis are mostly helpful commercialized products or generative AI, leading to a cultural mindset where AI is an assistant capable of autonomous tasks. Popular fictional depictions of artificial intelligence clearly demonstrate that those perceptions of threats fall closely in line with the sorts of actions portrayed by AI characters, suggesting that pop media has a significant influence over its audience’s understanding of AI technology and its potential ramifications. To mitigate harm that AI tools can inflict upon the general public, there is an immediate need for technology-specific legislation, incentives and deterrents, and oversight so that artificial intelligence can be regulated and controlled.
ContributorsCrowe, Katlynn (Author) / Martin, Thomas (Thesis director) / Anderson, Lisa (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2024-05
Description
This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes. Further, almost all of these games are played on a rectangular grid. Contrarily, this project develops an AI player, referred to as GG-net, to play the online strategy game Warzone, which is based on the classic board game Risk. Warzone is played on a wide variety of irregularly shaped maps. Prior research has struggled to create an effective AI for Risk-like games due to the immense branching factor. The most successful attempts tended to rely on manually restricting the set of actions the AI considered while also engineering useful features for the AI to consider. GG-net uses no human knowledge, but rather a genetic algorithm combined with a graph neural network. Together, these methods allow GG-net to perform competitively across a multitude of maps. GG-net outperformed the built-in rule-based AI by 413 Elo (representing an 80.7% chance of winning) and an approach based on AlphaZero using graph neural networks by 304 Elo (representing a 74.2% chance of winning). This same advantage holds across both seen and unseen maps. GG-net appears to be a strong opponent on both small and medium maps, however, on large maps with hundreds of territories, inefficiencies in GG-net become more significant and GG-net struggles against the rule-based approach. Overall, GG-net was able to successfully learn the game and generalize across maps of a similar size, albeit further work is required for GG-net to become more successful on large maps.
ContributorsBauer, Andrew (Author) / Yang, Yezhou (Thesis director) / Harrison, Blake (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
Description
“Social Sports is an application which facilitates the environment fans need to support their teams, in doing so our application aids hospitality businesses market their events and brings business during their downtime. Social Sports allows businesses to market their sports screening events to fans and supporters. Fans and supporters using Social Sports are able to see the percentage of supporters/fans on each side and decide which bar or restaurant to go watch the game. Social Sport’s mission is to connect sports fans with other like minded passionate fans and enable community formation and allow sports fans around the world to socialize with much ease.”
ContributorsWood, Alexander (Author) / Rodin, Dawson (Co-author) / Bhargana, Akshat (Co-author) / Cheshire, Ashley (Co-author) / Fuller, Sarah (Co-author) / Byrne, Jared (Thesis director) / Thomasson, Anna (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-05
Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05