Neurosymbolic automated story generation

dc.contributor.advisor Riedl, Mark O.
dc.contributor.author Martin, Lara Jean
dc.contributor.committeeMember Howard, Ayanna
dc.contributor.committeeMember Goel, Ashok
dc.contributor.committeeMember Parikh, Devi
dc.contributor.committeeMember Black, Alan W.
dc.contributor.department Interactive Computing
dc.date.accessioned 2021-06-10T13:57:38Z
dc.date.available 2021-06-10T13:57:38Z
dc.date.created 2021-05
dc.date.issued 2021-04-07
dc.date.submitted May 2021
dc.date.updated 2021-06-10T13:57:39Z
dc.description.abstract Although we are currently riding a technological wave of personal assistants, many of these agents still struggle to communicate appropriately. Humans are natural storytellers, so it would be fitting if artificial intelligence (AI) could tell stories as well. Automated story generation is an area of AI research that aims to create agents that tell good stories. With goodness being subjective and hard-to-define, I focus on the perceived coherence of stories in this thesis. Previous story generation systems use planning and symbolic representations to create new stories, but these systems require a vast amount of knowledge engineering. The stories created by these systems are coherent, but only a finite set of stories can be generated. In contrast, very large neural language models have recently made the headlines in the natural language processing community. Though impressive on the surface, even the most sophisticated of these models begins to lose coherence over time. My research looks at both neural and symbolic techniques of automated story generation. In this dissertation, I created automated story generation systems that improved coherence by leveraging various symbolic approaches for neural systems. I did this through a collection of techniques; by separating out semantic event generation from syntactic sentence generation, manipulating neural event generation to become goal-driven, improving syntactic sentence generation to be more interesting and coherent, and creating a rule-based infrastructure to aid neural networks in causal reasoning.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/64643
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Automated story generation
dc.subject Neural story generation
dc.subject Neurosymbolic systems
dc.subject Natural language generation
dc.title Neurosymbolic automated story generation
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Riedl, Mark O.
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Interactive Computing
relation.isAdvisorOfPublication 6512b353-3315-4dd1-9f47-7aaef3e19300
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication aac3f010-e629-4d08-8276-81143eeaf5cc
thesis.degree.level Doctoral
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