Reasoning and Learning with Probabilistic Answer Set Programming
|Abstract||Knowledge Representation (KR) is one of the prominent approaches to Artificial Intelligence (AI) that is concerned with representing knowledge in a form that computer systems can utilize to solve complex problems. Answer Set Programming (ASP), based on the stable model semantics, is a widely-used KR framework that facilitates elegant and efficient representations for many problem domains that require complex reasoning.
However, while ASP is effective on deterministic problem domains, it is not suitable for applications involving quantitative uncertainty, for example, those that require probabilistic reasoning. Furthermore, it is hard to utilize information that can be statistically induced from data with ASP problem modeling.
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|Contributor||Wang, Yi (Author) / Lee, Joohyung (Advisor) / Baral, Chitta (Committee member) / Kambhampati, Subbarao (Committee member) / Natarajan, Sriraam (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)|
|Subject||Computer science / Answer Set Programming / Artificial Intelligence / Automated Reasoning / Knowledge Representation / Probabilistic Reasoning / Statistical Relational Learning|
|Note||Doctoral Dissertation Computer Science 2019|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|