Logic-based Approaches in Explainable AI and Natural Language Understanding

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dc.contributor.ORCID0000-0003-2460-3132 (Shakerin, F)
dc.contributor.advisorGupta, Gopal
dc.creatorShakerin, Farhad
dc.date.accessioned2020-09-03T14:58:37Z
dc.date.available2020-09-03T14:58:37Z
dc.date.created2020-05
dc.date.issued2020-05
dc.date.submittedMay 2020
dc.date.updated2020-09-03T14:58:37Z
dc.description.abstractDramatic success of machine learning algorithms has led to a torrent of Artificial Intelligence (AI) applications in computer vision and natural language understanding. However, the effectiveness of these systems is limited by the machines’ current inability to explain and justify their decisions and actions. The Explainable AI program (Gunning, 2015) aims at creating a suite of machine learning techniques that: a) Produces explainable models without sacrificing predictive performance b) Enables human users to understand the underlying logic and diagnose the mistakes made by the AI system. Inspired by Explainable AI program, this dissertation presents logic programming-based approaches to some of the problems of interest in Explainable AI including learning machine learning hypotheses in the form of default theories, counter-factual reasoning and natural language understanding. In particular, We introduce algorithms that automate learning of default theories. We leverage these algorithms to capture the underlying logic of complex statistical learning models. We also propose a fully explainable logic programming-based framework for visual question answering and introduce a counter-factual reasoner based on Craig Interpolants and Answer Set Programming to come up with recommendations that respect logical, physical, and temporal constraints.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/8845
dc.language.isoen
dc.rights©2020 Farhad Shakerin. All rights reserved.
dc.subjectLogic programming
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectNatural language processing (Computer science)
dc.subjectComputer logic
dc.titleLogic-based Approaches in Explainable AI and Natural Language Understanding
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentComputer Science
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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