Matching Items (31)
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- All Subjects: artificial intelligence
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
ContributorsKemmer, Ryan Wyeth (Author) / Escobedo, Adolfo (Thesis director) / Maciejewski, Ross (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis serves as a baseline for the potential for prediction through machine learning (ML) in baseball. Hopefully, it also will serve as motivation for future work to expand and reach the potential of sabermetrics, advanced Statcast data and machine learning. The problem this thesis attempts to solve is predicting the outcome of a pitch. Given proper pitch data and situational data, is it possible to predict the result or outcome of a pitch? The result or outcome refers to the specific outcome of a pitch, beyond ball or strike, but if the hitter puts the ball in play for a double, this thesis shows how I attempted to predict that type of outcome. Before diving into my methods, I take a deep look into sabermetrics, advanced statistics and the history of the two in Major League Baseball. After this, I describe my implemented machine learning experiment. First, I found a dataset that is suitable for training a pitch prediction model, I then analyzed the features and used some feature engineering to select a set of 16 features, and finally, I trained and tested a pair of ML models on the data. I used a decision tree classifier and random forest classifier to test the data. I attempted to us a long short-term memory to improve my score, but came up short. Each classifier performed at around 60% accuracy. I also experimented using a neural network approach with a long short-term memory (LSTM) model, but this approach requires more feature engineering to beat the simpler classifiers. In this thesis, I show examples of five hitters that I test the models on and the accuracy for each hitter. This work shows promise that advanced classification models (likely requiring more feature engineering) can provide even better prediction outcomes, perhaps with 70% accuracy or higher! There is much potential for future work and to improve on this thesis, mainly through the proper construction of a neural network, more in-depth feature analysis/selection/extraction, and data visualization.
ContributorsGoodman, Avi (Author) / Bryan, Chris (Thesis director) / Hsiao, Sharon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis examines the applications of the Internet of Things and Artificial Intelligence within small-to-medium sized retail businesses. These technologies have become a common aspect of a modern business environment, yet there remains a level of unfamiliarity with these concepts for business owners to fully utilize these tools. The complexity behind IoT and AI has been simplified to provide benefits for a brick and mortar business store in regards to security, logistics, profit optimization, operations, and analytics. While these technologies can contribute to a business’s success, they potentially come with a high and unattainable financial cost. In order to investigate which aspects of businesses can benefit the most from these technologies, interviews with small-to-medium business owners were conducted and paired with an analysis of published research. These interviews provided specific pain points and issues that could potentially be solved by these technologies. The analysis conducted in this thesis gives a detailed summary of this research and provides a business model for two small businesses to optimize their Internet of Things and Artificial Intelligence to solve these pain points, while staying in their financial budget.
ContributorsAldrich, Lauren (Co-author) / Bricker, Danielle (Co-author) / Sebold, Brent (Thesis director) / Vermeer, Brandon (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
While there are many existing systems which take natural language descriptions and use them to generate images or text, few systems exist to generate 3d renderings or environments based on natural language. Most of those systems are very limited in scope and require precise, predefined language to work, or large well tagged datasets for their models. In this project I attempt to apply concepts in NLP and procedural generation to a system which can generate a rough scene estimation of a natural language description in a 3d environment from a free use database of models. The primary objective of this system, rather than a completely accurate representation, is to generate a useful or interesting result. The use of such a system comes in assisting designers who utilize 3d scenes or environments for their work.
ContributorsHann, Jacob R. (Author) / Kobayashi, Yoshihiro (Thesis director) / Srivastava, Siddharth (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done with industry standard performance technology and protocols to create an accessible interface for creative expression. Artificial intelligence models were created to generate art based on simple text inputs. Users were then invited to display their creativity using the software, and a comprehensive performance showcased the potential of the system for artistic expression.
ContributorsPardhe, Joshua (Author) / Lim, Kang Yi (Co-author) / Meuth, Ryan (Thesis director) / Brian, Jennifer (Committee member) / Hermann, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Artistic expression can be made more accessible through the use of technological interfaces such as auditory analysis, generative artificial intelligence models, and simplification of complicated systems, providing a way for human driven creativity to serve as an input that allow users to creatively express themselves. Studies and testing were done with industry standard performance technology and protocols to create an accessible interface for creative expression. Artificial intelligence models were created to generate art based on simple text inputs. Users were then invited to display their creativity using the software, and a comprehensive performance showcased the potential of the system for artistic expression.
ContributorsLim, Kang Yi (Author) / Pardhe, Joshua (Co-author) / Meuth, Ryan (Thesis director) / Brian, Jennifer (Committee member) / Hermann, Kristen (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
As threats emerge, change, and grow, the life of a police officer continues to intensify. To help support police training curriculums and police cadets through this critical career juncture, this study proposes a state of the art approach to stress prediction and intervention through wearable devices and machine learning models. As an integral first step of a larger study, the goal of this research is to provide relevant information to machine learning models to formulate a correlation between stress and police officers’ physiological responses on and off on the job. Fitbit devices were leveraged for data collection and were complemented with a custom built Fitbit application, called StressManager, and study dashboard, termed StressWatch. This analysis uses data collected from 15 training cadets at the Phoenix Police Regional Training Academy over a 13 week span. Close collaboration with these participants was essential; the quality of data collection relied on consistent “syncing” and troubleshooting of the Fitbit devices. After the data were collected and cleaned, features related to steps, calories, movement, location, and heart rate were extracted from the Fitbit API and other supplemental resources and passed through to empirically chosen machine learning models. From the results of these models, we formulate that events of increased intensity combined with physiological spikes contribute to the overall stress perception of a police training cadet
ContributorsParanjpe, Tara (Author) / Zhao, Ming (Thesis director) / Roberts, Nicole (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
For my Honors Thesis, I decided to create an Artificial Intelligence Project to predict Fantasy NFL Football Points of players and team's defense. I created a Tensorflow Keras AI Regression model and created a Flask API that holds the AI model, and a Django Try-It Page for the user to use the model. These services are hosted on ASU's AWS service. In my Flask API, it actively gathers data from Pro-Football-Reference, then calculates the fantasy points. Let’s say the current year is 2022, then the model analyzes each player and trains on all data from available from 2000 to 2020 data, tests the data on 2021 data, and predicts for 2022 year. The Django Website asks the user to input the current year, then the user clicks the submit button runs the AI model, and the process explained earlier. Next, the user enters the player's name for the point prediction and the website predicts the last 5 rows with 4 being the previous fantasy points and the 5th row being the prediction.
ContributorsPanikulam, Caleb (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
With the increasing presence and importance of machine learning, artificial intelligence, and big data in our daily lives, there comes the necessity to re-evaluate how magical, enchanted lines of thinking may or may not survive alongside the turn of the century. There exists a set of connections between magic and the aforementioned field of technology, in that this specific field has the potential to become sufficiently advanced and complex as to cause unpredictable problems down the line. This discussion will explore several different topics ranging from the comparisons between magic and technology to the dangers of these systems being “black box” and rather ambiguous in how they turn data input into prediction output, all central to the idea that this increasingly tech-focused world should be thought about in a magical and re-enchanted way, especially as legislation is drafted up and decided upon that can determine how these impressive new technologies will be regulated going forward.
ContributorsRodi, Michael (Author) / Ostling, Michael (Thesis director) / Blanco, Eduardo (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05