Matching Items (49)
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- All Subjects: artificial intelligence
- All Subjects: Virtual Reality
- Creators: Computer Science and Engineering Program
Description
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
Penetration testing is regarded as the gold-standard for understanding how well an organization can withstand sophisticated cyber-attacks. However, the recent prevalence of markets specializing in zero-day exploits on the darknet make exploits widely available to potential attackers. The cost associated with these sophisticated kits generally precludes penetration testers from simply obtaining such exploits – so an alternative approach is needed to understand what exploits an attacker will most likely purchase and how to defend against them. In this paper, we introduce a data-driven security game framework to model an attacker and provide policy recommendations to the defender. In addition to providing a formal framework and algorithms to develop strategies, we present experimental results from applying our framework, for various system configurations, on real-world exploit market data actively mined from the darknet.
ContributorsRobertson, John James (Author) / Shakarian, Paulo (Thesis director) / Doupe, Adam (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This project created a tool for visualizing constructive solid geometry (CSG) using an HTC Vive virtual reality
headset. This tool provides functionality for surface triangulation
of a variety of three-dimensional primitive solids. Then with those
solids it can perform the core CSG operations—intersection,
union and complement—to create more complex objects. This
tool also parses in Silo data files to allow the visualization
of scientific models like the Annular Core Research Reactor.
This project is useful for both education and visualization. This
project will be used by scientists to visualize and understand
their simulation results, and used as a museum exhibit to engage
the next generation of scientists in computer modeling.
headset. This tool provides functionality for surface triangulation
of a variety of three-dimensional primitive solids. Then with those
solids it can perform the core CSG operations—intersection,
union and complement—to create more complex objects. This
tool also parses in Silo data files to allow the visualization
of scientific models like the Annular Core Research Reactor.
This project is useful for both education and visualization. This
project will be used by scientists to visualize and understand
their simulation results, and used as a museum exhibit to engage
the next generation of scientists in computer modeling.
ContributorsJones, Derek Matthew (Author) / Kashiwagi, Dean (Thesis director) / O'Brien, Matthew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Virtual reality gives users the opportunity to immerse themselves in an accurately
simulated computer-generated environment. These environments are accurately simulated in that they provide the appearance of- and allow users to interact with- the simulated environment. Using head-mounted displays, controllers, and auditory feedback, virtual reality provides a convincing simulation of interactable virtual worlds (Wikipedia, “Virtual reality”). The many worlds of virtual reality are often expansive, colorful, and detailed. However, there is one great flaw among them- an emotion evoked in many users through the exploration of such worlds-loneliness.
The content in these worlds is impressive, immersive, and entertaining. Without other people to share in these experiences, however, one can find themselves lonely. Users discover a feeling that no matter how many objects and colors surround them in countless virtual worlds, every world feels empty. As humans are social beings by nature, they feel lost without a sense of human connection and human interaction. Multiplayer experiences offer this missing element into the immersion of virtual reality worlds. Multiplayer offers users the opportunity to interact with other live people in a virtual simulation, which creates lasting memories and deeper, more meaningful immersion.
simulated computer-generated environment. These environments are accurately simulated in that they provide the appearance of- and allow users to interact with- the simulated environment. Using head-mounted displays, controllers, and auditory feedback, virtual reality provides a convincing simulation of interactable virtual worlds (Wikipedia, “Virtual reality”). The many worlds of virtual reality are often expansive, colorful, and detailed. However, there is one great flaw among them- an emotion evoked in many users through the exploration of such worlds-loneliness.
The content in these worlds is impressive, immersive, and entertaining. Without other people to share in these experiences, however, one can find themselves lonely. Users discover a feeling that no matter how many objects and colors surround them in countless virtual worlds, every world feels empty. As humans are social beings by nature, they feel lost without a sense of human connection and human interaction. Multiplayer experiences offer this missing element into the immersion of virtual reality worlds. Multiplayer offers users the opportunity to interact with other live people in a virtual simulation, which creates lasting memories and deeper, more meaningful immersion.
ContributorsJorgensen, Nicholas Keith (Co-author) / Jorgensen, Caitlin Nicole (Co-author) / Selgrad, Justin (Thesis director) / Ehgner, Arnaud (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This architecture was designed modularly, allowing the use of different planners and robots. The system automatically replans when robots connect or disconnect. The system was demonstrated on two real robots, a Fetch and a PeopleBot, by conducting a surveillance task on the fifth floor of the Computer Science building at Arizona State University. The next part of the system includes extensions for teaming with humans. An Android application was created to serve as the interface between the system and human teammates. This application provides a way for the system to communicate with humans in the loop. In addition, it sends location information of the human teammates to the system so that goal recognition can be performed. This goal recognition allows the generation of human-aware plans. This capability was demonstrated in a mock search and rescue scenario using the Fetch to locate a missing teammate.
ContributorsSaba, Gabriel Christer (Author) / Kambhampati, Subbarao (Thesis director) / Doupé, Adam (Committee member) / Chakraborti, Tathagata (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
One of the core components of many video games is their artificial intelligence. Through AI, a game can tell stories, generate challenges, and create encounters for the player to overcome. Even though AI has continued to advance through the implementation of neural networks and machine learning, game AI tends to implement a series of states or decisions instead to give the illusion of intelligence. Despite this limitation, games can still generate a wide range of experiences for the player. The Hybrid Game AI Framework is an AI system that combines the benefits of two commonly used approaches to developing game AI: Behavior Trees and Finite State Machines. Developed in the Unity Game Engine and the C# programming language, this AI Framework represents the research that went into studying modern approaches to game AI and my own attempt at implementing the techniques learned. Object-oriented programming concepts such as inheritance, abstraction, and low coupling are utilized with the intent to create game AI that's easy to implement and expand upon. The final goal was to create a flexible yet structured AI data structure while also minimizing drawbacks by combining Behavior Trees and Finite State Machines.
ContributorsRamirez Cordero, Erick Alberto (Author) / Kobayashi, Yoshihiro (Thesis director) / Nelson, Brian (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05