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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Ph.D. Dissertation Defense Announcement
Title: Learning Knowledge to Support Domain-Independent Narrative Intelligence
Boyang "Albert" Li
School of Interactive Computing
College of Computing
Georgia Institute of Technology
Date: Friday, November 14, 2014
Time: 2-4pm EST
Location: TSRB 223
Committee:
Dr. Mark O. Riedl (Advisor; School of Interactive Computing, Georgia Institute of Technology)
Dr. Jacob Eisenstein (School of Interactive Computing, Georgia Institute of Technology)
Dr. Ashok Goel (School of Interactive Computing, Georgia Institute of Technology)
Dr. Brian Magerko (School of Literature, Media, and Communication, Georgia Institute of Technology)
Dr. Stacy Marsella (College of Computer and Information Science, Northeastern University)
Abstract:
Narrative Intelligence is the ability to craft, tell and understand narratives. It has been proposed as a central component of machines aiming to understand human activities or to communicate effectively with humans. However, most existing systems purported to demonstrate narrative intelligence rely on manually authored knowledge structures that require extensive expert labor. These systems are constrained to operate in a few domains where knowledge has been provided.
This dissertation investigates the learning of knowledge structures to support Narrative Intelligence in any domain. I propose a system that, from an input corpus of simple exemplar stories, learns complex knowledge structures that subsequently enable the creation, telling, and understanding of stories. The knowledge representation balances the complexity of learning and the richness of narrative applications, so that we can (1) learn the knowledge robustly in the presence of noise, (2) generate a large variety of highly coherent stories, (3) tell them in recognizably different narration styles and (4) understand stories efficiently. The accuracy and effectiveness of the system have been verified by a series of user studies and computational experiments.
As a result, the system is able to demonstrate Narrative Intelligence in any domain where we can collect a small number of exemplar stories. This dissertation is the first step toward scaling computational narrative intelligence to meet the challenges of the real world.
