Matching Items (4)
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- All Subjects: Empathy
- 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
There are many anecdotal stories of dogs rescuing their owners from dangerous situations, but this rescue behavior has yet to be shown in an experimental setting. Studies have shown that dogs behave differently towards crying humans, but do not seek help for their owners when they are in distress. This study sought to determine if a dog could recognize when its owner was in distress and would attempt to rescue the owner. The experiment consisted of three conditions: a distress condition to determine how dogs respond to an owner calling for help, a reading condition to control for proximity-seeking and sound, and a food control to use as a basis for motivation and door-opening ability of the dog. Sixty dogs were tested in all three conditions in a pseudo-random order so that an equal number of dogs completed the conditions in each order. 38% of the dogs opened the apparatus for any condition, while 32% opened for the food and distress conditions and 27% opened for the reading condition, which shows that rescue in general is unlikely. There was no significant difference in the proportion of dogs who opened the apparatus for each condition, indicating that dogs are no more likely to rescue their distressed owners than they are to open the apparatus for other conditions and may not be able to sense that the owner is in distress. The similarities in the success rates also show that the owner can be just as motivating for a dog as food. Overall, the low success rates suggest that dogs are not generally likely to rescue a person who is trapped, even when they are calling for help.
ContributorsPatterson, Jordan Elizabeth (Author) / Wynne, Clive (Thesis director) / McBeath, Michael (Committee member) / W.P. Carey School of Business (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project is meant to measure and assess empathy through the Empathy Assessment Index (EAI) and Social Empathy Index (SEI) instruments. Researchers believe that empathy is an involuntary but dynamic aspect of people's affective and cognitive responses to emotional stimuli. This project used the EAI and SEI instruments to see whether a course taught at Arizona State University \u2014 PAF 300 \u2014increased empathy and its seven components within students. The results suggest that different modular interventions were effective in increasing four of the seven empathic components \u2014 affective response, perspective-taking, contextual understanding of systemic barriers, and macro self-other awareness/ perspective-taking \u2014 but that it was detrimental to two components, self-other awareness and affective mentalizing. Future studies are necessary to understand how aspects of a course curriculum can target and increase the seven components in individuals, as well as how these components relate to one another within the greater concept of empathy. Still, this research is important in the greater scheme of empathy as it seeks to understand and expand individuals' empathic levels in an increasingly bleak and desolate political climate.
ContributorsPirkl, Audrie Madison (Author) / Johnston, Erik W., 1977- (Thesis director) / Minrichs, Margaret (Committee member) / W.P. Carey School of Business (Contributor) / School of Public Affairs (Contributor) / Department of Management and Entrepreneurship (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