A data-driven approach for personalized drama management

Yu, Hong

Title:

A data-driven approach for personalized drama management

dc.contributor.advisor	Riedl, Mark O.
dc.contributor.author	Yu, Hong
dc.contributor.committeeMember	Isbell, Charles
dc.contributor.committeeMember	Magerko, Brian
dc.contributor.committeeMember	Roberts, David
dc.contributor.committeeMember	Thomaz, Andrea
dc.contributor.department	Interactive Computing
dc.date.accessioned	2015-09-21T14:24:27Z
dc.date.available	2015-09-21T14:24:27Z
dc.date.created	2015-08
dc.date.issued	2015-05-13
dc.date.submitted	August 2015
dc.date.updated	2015-09-21T14:24:27Z
dc.description.abstract	An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the role of a character in a fictional virtual world. The interactive narrative systems usually employ a drama manager (DM), an omniscient background agent that monitors the fictional world and determines what will happen next in the players' story experience. Prevailing approaches to drama management choose successive story plot points based on a set of criteria given by the game designers. In other words, the DM is a surrogate for the game designers. In this dissertation, I create a data-driven personalized drama manager that takes into consideration players' preferences. The personalized drama manager is capable of (1) modeling the players' preference over successive plot points from the players' feedback; (2) guiding the players towards selected plot points without sacrificing players' agency; (3) choosing target successive plot points that simultaneously increase the player's story preference ratings and the probability of the players selecting the plot points. To address the first problem, I develop a collaborative filtering algorithm that takes into account the specific sequence (or history) of experienced plot points when modeling players' preferences for future plot points. Unlike the traditional collaborative filtering algorithms that make one-shot recommendations of complete story artifacts (e.g., books, movies), the collaborative filtering algorithm I develop is a sequential recommendation algorithm that makes every successive recommendation based on all previous recommendations. To address the second problem, I create a multi-option branching story graph that allows multiple options to point to each plot point. The personalized DM working in the multi-option branching story graph can influence the players to make choices that coincide with the trajectories selected by the DM, while gives the players the full agency to make any selection that leads to any plot point in their own judgement. To address the third problem, the personalized DM models the probability that the players transitioning to each full-length stories and selects target stories that achieve the highest expected preference ratings at every branching point in the story space. The personalized DM is implemented in an interactive narrative system built with choose-your-own-adventure stories. Human study results show that the personalized DM can achieve significantly higher preference ratings than non-personalized DMs or DMs with pre-defined player types, while preserve the players' sense of agency.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/53851
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Personalized drama manager
dc.subject	Interactive narrative
dc.subject	Player modeling
dc.subject	Prefix based collaborative filtering
dc.title	A data-driven approach for personalized drama management
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