Matching Items (3)
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- All Subjects: Machine Learning
- 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
Fabrication of Alignment and Chemical Gradient Scaffold for Tendon-Bone Repair using Electrospinning
Description
Heterogeneous musculoskeletal tissues, such as the tendon-bone junction, is crucial for transferring mechanical loading during human physical activity. This region, also known as the enthesis, is composed of a complex extracellular matrix with gradient fiber orientations and chemistries. These different physical and chemical properties are crucial in providing the support that these junctions need in handling mechanical loading of everyday activities. Currently, surgical restorative procedures for a torn enthesis entail a very invasive technique of suturing the torn tendon onto the bone. This results in improper reinjury. To circumvent this issue, one common strategy within tissue engineering is to introduce a biomaterial scaffold which acts as a template for the local damaged tissue. Electrospinning can be utilized to fabricate a fibrous material to recapitulate the structure of the extracellular matrix. Currently electrospinning techniques only allow the creation of scaffold that consists of only one orientation and material. In this work, we investigated a multicomponent, magnetically assisted, electrospinning technique to fabricate a fiber alignment and chemical gradient scaffold for tendon-bone repair
ContributorsLe, Minh (Author) / Holloway, Julianne (Thesis director) / Green, Matthew (Committee member) / W.P. Carey School of Business (Contributor) / Chemical Engineering Program (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