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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.

This dissert... (more)
Created Date 2019
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
Type Doctoral Dissertation
Extent 262 pages
Language English
Note Doctoral Dissertation Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis