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- Creators: Chen, Yinong
Description
Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former.
ContributorsPalla, Ravi (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Kambhampati, Subbarao (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2012
Description
A common challenge faced by students is that they often have questions about course material that they cannot ask during lecture time. There are many ways for students to have these questions answered, such as office hours and online discussion boards. However, office hours may be at inconvenient times or locations, and online discussion boards are difficult to navigate and may be inactive. The purpose of this project was to create an Alexa skill that allows users to ask their Alexa-equipped device a question concerning their course material and to receive an answer retrieved from discussion board data. User questions are mapped to discussion board posts by use of the cosine similarity algorithm. In this algorithm, posts from the discussion board and the user’s question are converted into mathematical vectors, with each term in the vector corresponding to a word. The values of these terms are computed based on the word’s frequency within the vector’s corresponding document, the frequency of that word within all the documents, and the length of the document. After the question and candidate posts are converted into vectors, the algorithm determines the post most similar to the user’s question by computing the angle between the vectors. With the most similar discussion board post determined, the user receives the replies to the post, if any, as their answer. Users are able to indicate to their Alexa device whether they were satisfied by the answer, and if they were unsatisfied then they are given the opportunity to either rephrase their question or to have the question sent to a database of unanswered questions. The professor can view and answer the questions in this database on a website hosted by use of Amazon’s Simple Storage Service. The Alexa skill does well at answering questions that have already been asked in the discussion board. However, the skill depends heavily on the user’s word choice. Two questions that are semantically identical but different in phrasing are often given different answers. This is because the cosine algorithm measures similarity on the basis of word overlap, not semantic meaning, and thus the application never truly “understands” what type of answer the user desires. Improving the performance of this Alexa skill will require a more advanced question answering algorithm, but the limitations of Amazon Web Services as a development platform make implementing such an algorithm difficult. Nevertheless, this project has created the basis of a question answering Alexa skill by demonstrating a feasible way that the resources offered by Amazon can be utilized in order to build such an application.
ContributorsBaker, Matthew Elias (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
The aim of this project was to provide college applicants with the ability to apply using a video instead of an essay. These videos are analyzed automatically and their scripts are taken and submitted with the application. This was implemented through the use of Amazon Web Services (AWS) and their S3 buckets along with their speech to text transcription service. This type of application process can give admissions teams the opportunity to get to know who will potentially be attending their university and allows the applicants to express themselves to admissions teams in a new and unique way.
ContributorsStephan, Meagan (Co-author) / Pratt, Devan (Co-author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12