Matching Items (46)
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- All Subjects: artificial intelligence
- All Subjects: Programming
- Creators: Computer Science and Engineering Program
- Status: Published
Description
Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.
ContributorsSaldyt, Lucas P (Author) / Ben Amor, Heni (Thesis director) / Pavlic, Theodore (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
ContributorsLobo, Ian (Co-author) / Koleber, Keith (Co-author) / Markabawi, Jah (Co-author) / Masud, Abdullah (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The objective of this research is to determine an approach for automating the learning of the initial lexicon used in translating natural language sentences to their formal knowledge representations based on lambda-calculus expressions. Using a universal knowledge representation and its associated parser, this research attempts to use word alignment techniques to align natural language sentences to the linearized parses of their associated knowledge representations in order to learn the meanings of individual words. The work includes proposing and analyzing an approach that can be used to learn some of the initial lexicon.
ContributorsBaldwin, Amy Lynn (Author) / Baral, Chitta (Thesis director) / Vo, Nguyen (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. A crucial phase of food inspection is the identification of foreign particles found in the sample, such as insect body parts. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach to identifying species is visual examination by human analysts; this method is rather subjective and time-consuming. Furthermore, confident identification requires extensive experience and training. To aid this inspection process, we have developed in collaboration with FDA analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated pan, zoom, and color analysis capabilities. After species analysis, the results panel allows the user to compare the analyzed images with referenced images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface. Additional issues to address include standardization of image layout, extension of the feature-extraction algorithm, and utilizing image classification to build a central search engine for widespread usage.
ContributorsMartin, Daniel Luis (Author) / Ahn, Gail-Joon (Thesis director) / Doupé, Adam (Committee member) / Xu, Joshua (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can accomplish this task by giving the player a large toolset for expression in the plot. LudoNarrare, an engine for interactive storytelling, puts "verbs" in this toolset. Verbs are contextual choices of action given to agents in a story that result in narrative events. This paper begins with an analysis and statement of the problem of creating interactive stories. From here, various attempts to solve this problem, ranging from commercial video games to academic research, are given a brief overview to give context to what paths have already been forged. With the background set, the model of interactive storytelling that the research behind LudoNarrare led to is exposed in detail. The section exploring this model contains explanations on what storyworlds are and how they are structured. It then discusses the way these storyworlds can be brought to life. The exposition on the LudoNarrare model finally wraps up by considering the way storyworlds created around this model can be designed. After the concepts of LudoNarrare are explored in the abstract, the story of the engine's research and development and the specifics of its software implementation are given. With LudoNarrare fully explained, the focus then turns to plans for evaluation of its quality in terms of entertainment value, robustness, and performance. To conclude, possible further paths of investigation for LudoNarrare and its model of interactive storytelling are proposed to inspire those who wish to continue in the spirit of the project.
ContributorsStark, Joshua Matthew (Author) / VanLehn, Kurt (Thesis director) / Wetzel, Jon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Education in computer science is a difficult endeavor, with learning a new programing language being a barrier to entry, especially for college freshman and high school students. Learning a first programming language requires understanding the syntax of the language, the algorithms to use, and any additional complexities the language carries. Often times this becomes a deterrent from learning computer science at all. Especially in high school, students may not want to spend a year or more simply learning the syntax of a programming language. In order to overcome these issues, as well as to mitigate the issues caused by Microsoft discontinuing their Visual Programming Language (VPL), we have decided to implement a new VPL, ASU-VPL, based on Microsoft's VPL. ASU-VPL provides an environment where users can focus on algorithms and worry less about syntactic issues. ASU-VPL was built with the concepts of Robot as a Service and workflow based development in mind. As such, ASU-VPL is designed with the intention of allowing web services to be added to the toolbox (e.g. WSDL and REST services). ASU-VPL has strong support for multithreaded operations, including event driven development, and is built with Microsoft VPL users in mind. It provides support for many different robots, including Lego's third generation robots, i.e. EV3, and any open platform robots. To demonstrate the capabilities of ASU-VPL, this paper details the creation of an Intel Edison based robot and the use of ASU-VPL for programming both the Intel based robot and an EV3 robot. This paper will also discuss differences between ASU-VPL and Microsoft VPL as well as differences between developing for the EV3 and for an open platform robot.
ContributorsDe Luca, Gennaro (Author) / Chen, Yinong (Thesis director) / Cheng, Calvin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
ContributorsMasud, Abdullah Bin (Co-author) / Koleber, Keith (Co-author) / Lobo, Ian (Co-author) / Markabawi, Jah (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Gamification is used to provide an entertaining alternative to educate an individual on a topic that has proven to be difficult, confusing, or undesirable. This thesis describes the design of a video game whose goal was to provide a way for coders and non-coders to educate themselves on programming scopes while also being entertained in the process. Reaching the goal required using the puzzle genre to create a concept where programming scopes would serve as the primary mechanic while also using various other programming concepts to complement it. These concepts include variables, values, functions, programming statements, and conditions.
In order to ensure that the game worked both as an educational tool as well as an entertaining one, informal testers were used with various degrees of experience in both coding and video games. After reaching the end of the game, each of the testers demonstrated that they understood the programming concepts in their video game form. However, this understanding came after additional verbal help was supplied and illustrated that the tutorial section of the game would need to be re-worked in order to efficiently demonstrate each concept.
In order to ensure that the game worked both as an educational tool as well as an entertaining one, informal testers were used with various degrees of experience in both coding and video games. After reaching the end of the game, each of the testers demonstrated that they understood the programming concepts in their video game form. However, this understanding came after additional verbal help was supplied and illustrated that the tutorial section of the game would need to be re-worked in order to efficiently demonstrate each concept.
ContributorsLucero, Elijah Ray (Author) / Bazzi, Rida (Thesis director) / Selgrad, Justin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The goal of this product was to create a highly customizable application in which any individual, musician or not, can create a harmony for the user’s melody. This Automating Music Composer is built on the underlying rules of music composition, rules that are unique for each type of music available. This program is built on rules that are similar to how a Finite State Machine works (Fig 1). Each state represents a different chord in a given key, where the first roman numeral represents the first note in the chord progression. Each transition represents the action that can be taken by the chord progression, or the next note that can be reached by the current note. The user is able to manipulate these rules and styles, adjust different musical parameters to their liking, and is able to input their own melody, which then will output a unique harmony. This product aims to bridge the gap between predictive technologies and musical composition. Allowing the user to be more involved in the composition process helps the program to act as a tool for the user, rather than a separate entity that simply gives the user a completed recording. This allows the user to appreciate and understand what they are helping to produce more than they would if they were to simply be an inactive consumer of a random music composer. This product is meant to feel like an extension of the user, rather than a separate tool.
ContributorsKumar, Dhantin (Co-author) / Lopez, Christian (Co-author) / Nakamura, Mutsumi (Thesis director) / Blount, Andrew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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