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Answer Set Programming Modulo Theories


Abstract Knowledge representation and reasoning is a prominent subject of study within the field of artificial intelligence that is concerned with the symbolic representation of knowledge in such a way to facilitate automated reasoning about this knowledge. Often in real-world domains, it is necessary to perform defeasible reasoning when representing default behaviors of systems. Answer Set Programming is a widely-used knowledge representation framework that is well-suited for such reasoning tasks and has been successfully applied to practical domains due to efficient computation through grounding--a process that replaces variables with variable-free terms--and propositional solvers similar to SAT solvers. However, some domains provide a challenge ... (more)
Created Date 2016
Contributor Bartholomew, Michael James (Author) / Lee, Joohyung (Advisor) / Bazzi, Rida (Committee member) / Colbourn, Charles (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / answer set programming / defeasible / knowledge representation / nonmonotonic / reasoning / satisfiability modulo theories
Type Doctoral Dissertation
Extent 301 pages
Language English
Copyright
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Note Doctoral Dissertation Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis