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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Language
  • English
Date Range
2010 2020


Generative Adversarial Networks are designed, in theory, to replicate the distribution of the data they are trained on. With real-world limitations, such as finite network capacity and training set size, they inevitably suffer a yet unavoidable technical failure: mode collapse. GAN-generated data is not nearly as diverse as the real-world data the network is trained on; this work shows that this effect is especially drastic when the training data is highly non-uniform. Specifically, GANs learn to exacerbate the social biases which exist in the training set along sensitive axes such as gender and race. In an age where many datasets …

Contributors
Jain, Niharika, Kambhampati, Subbarao, Liu, Huan, et al.
Created Date
2020

The pervasive use of the Web has connected billions of people all around the globe and enabled them to obtain information at their fingertips. This results in tremendous amounts of user-generated data which makes users traceable and vulnerable to privacy leakage attacks. In general, there are two types of privacy leakage attacks for user-generated data, i.e., identity disclosure and private-attribute disclosure attacks. These attacks put users at potential risks ranging from persecution by governments to targeted frauds. Therefore, it is necessary for users to be able to safeguard their privacy without leaving their unnecessary traces of online activities. However, privacy …

Contributors
Beigi, Ghazaleh, Liu, Huan, Kambhampati, Subbarao, et al.
Created Date
2020

The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to …

Contributors
Manikonda, Lydia, Kambhampati, Subbarao, Liu, Huan, et al.
Created Date
2019

Allocating tasks for a day's or week's schedule is known to be a challenging and difficult problem. The problem intensifies by many folds in multi-agent settings. A planner or group of planners who decide such kind of task association schedule must have a comprehensive perspective on (1) the entire array of tasks to be scheduled (2) idea on constraints like importance cum order of tasks and (3) the individual abilities of the operators. One example of such kind of scheduling is the crew scheduling done for astronauts who will spend time at International Space Station (ISS). The schedule for the …

Contributors
MIshra, Aditya Prasad, Kambhampati, Subbarao, Chiou, Erin, et al.
Created Date
2019

Knowledge Representation (KR) is one of the prominent approaches to Artificial Intelligence (AI) that is concerned with representing knowledge in a form that computer systems can utilize to solve complex problems. Answer Set Programming (ASP), based on the stable model semantics, is a widely-used KR framework that facilitates elegant and efficient representations for many problem domains that require complex reasoning. However, while ASP is effective on deterministic problem domains, it is not suitable for applications involving quantitative uncertainty, for example, those that require probabilistic reasoning. Furthermore, it is hard to utilize information that can be statistically induced from data with …

Contributors
Wang, Yi, Lee, Joohyung, Baral, Chitta, et al.
Created Date
2019

A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of …

Contributors
Chakraborti, Tathagata, Kambhampati, Subbarao, Talamadupula, Kartik, et al.
Created Date
2018

Reasoning about actions forms the basis of many tasks such as prediction, planning, and diagnosis in a dynamic domain. Within the reasoning about actions community, a broad class of languages, called action languages, has been developed together with a methodology for their use in representing and reasoning about dynamic domains. With a few notable exceptions, the focus of these efforts has largely centered around single-agent systems. Agents rarely operate in a vacuum however, and almost in parallel, substantial work has been done within the dynamic epistemic logic community towards understanding how the actions of an agent may effect not just …

Contributors
Gelfond, Gregory, Baral, Chitta, Kambhampati, Subbarao, et al.
Created Date
2018

Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social …

Contributors
Morstatter, Fred, Liu, Huan, Kambhampati, Subbarao, et al.
Created Date
2017

Automated planning addresses the problem of generating a sequence of actions that enable a set of agents to achieve their goals.This work investigates two important topics from the field of automated planning, namely model-lite planning and multi-agent planning. For model-lite planning, I focus on a prominent model named Annotated PDDL and it's related application of robust planning. For this model, I try to identify a method of leveraging additional domain information (available in the form of successful plan traces). I use this information to refine the set of possible domains to generate more robust plans (as compared to the original …

Contributors
Sreedharan, Sarath, Kambhampati, Subbarao, Zhang, Yu, et al.
Created Date
2016

Humans and robots need to work together as a team to accomplish certain shared goals due to the limitations of current robot capabilities. Human assistance is required to accomplish the tasks as human capabilities are often better suited for certain tasks and they complement robot capabilities in many situations. Given the necessity of human-robot teams, it has been long assumed that for the robotic agent to be an effective team member, it must be equipped with automated planning technologies that helps in achieving the goals that have been delegated to it by their human teammates as well as in deducing …

Contributors
Narayanan, Vignesh, Kambhampati, Subbarao, Zhang, Yu, et al.
Created Date
2015