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- All Subjects: Communication
- All Subjects: Machine Learning
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Member of: Theses and Dissertations
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
This online course is designed to educate students on the most popular yet conflicting generations: Millennials and Generation Xers. The lectures discuss what is a generation, what makes someone a Millennial or Generation Xer, and explains how to communicate with each generation. Whether you are a manager, coworker, or student, this course will teach you how each generation views the world, and how you can effectively communicate and relate to both generations. Below is a link to take this course at a discounted price: https://www.udemy.com/generational-communication-in-the-workplace/?couponCode=COMMUNICATE
ContributorsTaylor, Charlotte Ann (Author) / Desch, Timothy (Thesis director) / Adame, Elissa (Committee member) / School of Music (Contributor) / The Design School (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
An information influx and numerous modes of content delivery has resulted in local governments competing for the public's attention. A recent poll from the Public Technology Institute discovered that although 85% of Local Governments use social media to disseminate information to their constituents, only 37% have an enterprise-wide social media strategy (PTI, 2017). Without a clear approach towards social media, Local Governments are failing to maximize their voices and often ineffective when reaching out to their constituents. Research has suggested, charisma is a successful tool for capturing an audience's attention and conveying a memorable message. Charisma can also be taught and executed not only through spoken rhetoric but in online social media platforms. Within this study, 18 local government employees participated in an educational workshop on the use of nine non-verbal "Charismatic Leadership Tactics". Participants completed a pre-workshop assignment which was later compared to a post-workshop assignment. Results showed, participants on average, increased their use of Charismatic Leadership Tactics by a mean of 61%. Researchers collected social media analytics one month prior and one month following the workshop from the City's social media accounts in which participants managed. Collectively, of the thirteen social media accounts, the overall total engagement was greater the month after the educational workshop compared to the month before the workshop. These results suggest charisma can be taught, charisma can be conveyed through micro-blogosphere platforms such as Twitter, and the use of Charismatic Leadership Tactics could be responsible for increasing follower engagement with social media content.
ContributorsPrior, Thomas Cole (Author) / Jensen, Ulrich (Thesis director) / Lewis, Natalie (Committee member) / School of Public Affairs (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Abstract: The Ultimate Fighting Championship or UFC as it is commonly known, was founded in 1993 and has quickly built itself into the world's foremost authority on all things MMA (mixed martial arts) related. With pay-per-view and cable television deals in hand, the UFC has become a huge competitor in the sports market, rivaling the popularity of boxing for almost a decade. As with most other sports, the UFC has seen an influx of various analytics and data science over the past five to seven years. We see this revolution in football with the broadcast first down markers, basketball with tracking player movement, and baseball with locating pitches for strikes and balls, and now the UFC has partnered with statistics company Fightmetric, to provide in-depth statistical analysis of its fights. ESPN has their win probability metrics, and statistical predictive modeling has begun to spread throughout sports. All these stats were made to showcase the information about a fighter that one wouldn't typically know, giving insight into how the fight might go. But, can these fights be predicted? Based off of the research of prior individuals and combining the thought processes of relevant research into other sports leagues, I sought to use the arsenal of statistical analyses done by Fightmetric, along with the official UFC fighter database to answer the question of whether UFC fights could be predicted. Specifically, by using only data that would be known about a fighter prior to stepping into the cage, could I predict with any degree of certainty who was going to win the fight?
ContributorsMoorman, Taylor D. (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05