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Description
TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all be set on a scenario-by-scenario basis. The taxis must attempt to service the fares as quickly as possible, by picking each one up and carrying it to its drop-off location. The TaxiWorld scenario is formally modeled using both Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) and Multi-agent Markov Decision Processes (MMDPs). The purpose of developing formal models is to learn how to build and use formal Markov models, such as can be given to planners to solve for optimal policies in problem domains. However, finding optimal solutions for Dec-POMDPs is NEXP-Complete, so an empirical algorithm was also developed as an improvement to the method already in use on the simulator, and the methods were compared in identical scenarios to determine which is more effective. The empirical method is of course not optimal - rather, it attempts to simply account for some of the most important factors to achieve an acceptable level of effectiveness while still retaining a reasonable level of computational complexity for online solving.
ContributorsWhite, Christopher (Author) / Kambhampati, Subbarao (Thesis advisor) / Gupta, Sandeep (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2011
Description
\English is a programming language, a method of allowing programmers to write instructions such that a computer may understand and execute said instructions in the form of a program. Though many programming languages exist, this particular language is designed for ease of development and heavy optimizability in ways that no other programming language is. Building on the principles of Assembly level efficiency, referential integrity, and high order functionality, this language is able to produce extremely efficient code; meanwhile, programmatically defined English-based reusable syntax and a strong, static type system make \English easier to read and write than many existing programming languages. Its generalization of all language structures and components to operators leaves the language syntax open to project-specific syntactical structuring, making it more easily applicable in more cases. The thesis project requirements came in three parts: a compiler to compile \English code into NASM Assembly to produce a final program product; a standard library to define many of the basic operations of the language, including the creation of lists; and C translation library that would utilize \English properties to compile C code using the \English compiler. Though designed and partially coded, the compiler remains incomplete. The standard library, C translation library, and design of the language were completed. Additional tools regarding the language design and implementation were also created, including a Gedit syntax highlighting configuration file; usage documentation describing in a tutorial style the basic usage of the language; and more. Though the thesis project itself may be complete, the \English project will continue in order to produce a new language capable of the abilities possible with the design of this language.
ContributorsDavey, Connor (Author) / Gupta, Sandeep (Thesis director) / Bazzi, Rida (Committee member) / Calliss, Debra (Committee member) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Cyber Physical Systems (CPSs) are systems comprising of computational systems that interact with the physical world to perform sensing, communication, computation and actuation. Common examples of these systems include Body Area Networks (BANs), Autonomous Vehicles (AVs), Power Distribution Systems etc. The close coupling between cyber and physical worlds in a CPS manifests in two types of interactions between computing systems and the physical world: intentional and unintentional. Unintentional interactions result from the physical characteristics of the computing systems and often cause harm to the physical world, if the computing nodes are close to each other, these interactions may overlap thereby increasing the chances of causing a Safety hazard. Similarly, due to mobile nature of computing nodes in a CPS planned and unplanned interactions with the physical world occur. These interactions represent the behavior of a computing node while it is following a planned path and during faulty operations. Both of these interactions change over time due to the dynamics (motion) of the computing node and may overlap thereby causing harm to the physical world. Lack of proper modeling and analysis frameworks for these systems causes system designers to use ad-hoc techniques thereby further increasing their design and development time. The thesis addresses these problems by taking a holistic approach to model Computational, Physical and Cyber Physical Interactions (CPIs) aspects of a CPS and proposes modeling constructs for them. These constructs are analyzed using a safety analysis algorithm developed as part of the thesis. The algorithm computes the intersection of CPIs for both mobile as well as static computing nodes and determines the safety of the physical system. A framework is developed by extending AADL to support these modeling constructs; the safety analysis algorithm is implemented as OSATE plug-in. The applicability of the proposed approach is demonstrated by considering the safety of human tissue during the operations of BAN, and the safety of passengers traveling in an Autonomous Vehicle.
ContributorsKandula, Sailesh Umamaheswara (Author) / Gupta, Sandeep (Thesis advisor) / Lee, Yann Hang (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2010
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
Computer science education is an increasingly vital area of study with various challenges that increase the difficulty level for new students resulting in higher attrition rates. As part of an effort to resolve this issue, a new visual programming language environment was developed for this research, the Visual IoT and Robotics Programming Language Environment (VIPLE). VIPLE is based on computational thinking and flowchart, which reduces the needs of memorization of detailed syntax in text-based programming languages. VIPLE has been used at Arizona State University (ASU) in multiple years and sections of FSE100 as well as in universities worldwide. Another major issue with teaching large programming classes is the potential lack of qualified teaching assistants to grade and offer insight to a student’s programs at a level beyond output analysis.
In this dissertation, I propose a novel framework for performing semantic autograding, which analyzes student programs at a semantic level to help students learn with additional and systematic help. A general autograder is not practical for general programming languages, due to the flexibility of semantics. A practical autograder is possible in VIPLE, because of its simplified syntax and restricted options of semantics. The design of this autograder is based on the concept of theorem provers. To achieve this goal, I employ a modified version of Pi-Calculus to represent VIPLE programs and Hoare Logic to formalize program requirements. By building on the inference rules of Pi-Calculus and Hoare Logic, I am able to construct a theorem prover that can perform automated semantic analysis. Furthermore, building on this theorem prover enables me to develop a self-learning algorithm that can learn the conditions for a program’s correctness according to a given solution program.
In this dissertation, I propose a novel framework for performing semantic autograding, which analyzes student programs at a semantic level to help students learn with additional and systematic help. A general autograder is not practical for general programming languages, due to the flexibility of semantics. A practical autograder is possible in VIPLE, because of its simplified syntax and restricted options of semantics. The design of this autograder is based on the concept of theorem provers. To achieve this goal, I employ a modified version of Pi-Calculus to represent VIPLE programs and Hoare Logic to formalize program requirements. By building on the inference rules of Pi-Calculus and Hoare Logic, I am able to construct a theorem prover that can perform automated semantic analysis. Furthermore, building on this theorem prover enables me to develop a self-learning algorithm that can learn the conditions for a program’s correctness according to a given solution program.
ContributorsDe Luca, Gennaro (Author) / Chen, Yinong (Thesis advisor) / Liu, Huan (Thesis advisor) / Hsiao, Sharon (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2020
Description
Languages, specially gestural and sign languages, are best learned in immersive environments with rich feedback. Computer-Aided Language Learning (CALL) solu- tions for spoken languages have successfully incorporated some feedback mechanisms, but no such solution exists for signed languages. Computer Aided Sign Language Learning (CASLL) is a recent and promising field of research which is made feasible by advances in Computer Vision and Sign Language Recognition(SLR). Leveraging existing SLR systems for feedback based learning is not feasible because their decision processes are not human interpretable and do not facilitate conceptual feedback to learners. Thus, fundamental research is needed towards designing systems that are modular and explainable. The explanations from these systems can then be used to produce feedback to aid in the learning process.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
ContributorsPaudyal, Prajwal (Author) / Gupta, Sandeep (Thesis advisor) / Banerjee, Ayan (Committee member) / Hsiao, Ihan (Committee member) / Azuma, Tamiko (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
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