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- All Subjects: journalism
- 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
As our relationship with technology continues to encourage people to spend more time engaged online, traditional means of journalism must adapt in order to communicate with audiences. While many news organizations default to social media outlets, the goal of this project is to allow users a more direct experience with reporters, photographers and editors. It will allow The State Press, the official, student-run news organization covering ASU, to create content within Slack, an internal messaging platform commonly used in newsrooms. Secondly, it will provide a means for viewers to conveniently ingest their news as it unfolds, with updates, media, and analysis appearing in front of them without having to refresh the page.
ContributorsQuigley, James Alan (Author) / Gary, Kevin (Thesis director) / Squire, Susan (Committee member) / Software Engineering (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
As the premier colligate summer league in the country, the Cape Cod Baseball League has operated since 1885 and has seen over one thousand all-time alumni step foot in professional baseball. Every season, each of the CCBL's ten teams call upon some of the nation's top aspiring sports broadcasters, writers, and social media managers to spearhead the coverage of the league and tell the stories of the summer. However, while the season offers hours of repetition and exposure to players and journalists alike, the league's coverage capabilities fall short of its high potential due to inconsistencies and inadequacies that restrain its media content from matching the level of baseball that takes place on the field. Through the identification of specific problems within the league's broadcast equipment, its varying platforms, and its growing gap between individual coverage teams, this thesis offers both short-term and long-term solutions that aim to raise the standards and capabilities of league content while also raising awareness of the issue itself. While considering the Cape Cod League's unique non-profit business model and its most recent financial situation, this thesis also illustrates opportunities within fundraising events, the league's online audience, and its vast alumni network that can create a sustainable business plan for the near and distant future of the Cape Cod Broadcast Network.
ContributorsKercheval, Kyle Nicholas (Author) / Kurland, Brett (Thesis director) / Cesmat, Brad (Committee member) / W.P. Carey School of Business (Contributor) / Walter Cronkite School of Journalism and Mass Communication (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