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Whodunit: A Generative Model for Murder Mysteries as an Information Game

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Whodunit: A Generative Model for Murder Mysteries as an Information Game

Saffarizadeh, Arian (2022) Whodunit: A Generative Model for Murder Mysteries as an Information Game. Masters thesis, Concordia University.

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Abstract

This research focuses on creating a model to generate information for a narrative-based game of
murder mysteries. The literature review aspect of this research will review previous related research
works in the subject of procedural narrative generation, explains the concept information game in
the context of murder mysteries as well as on how the narrative structure of murder mystery stories
in fiction can assist us in constructing a model that can generate information for a game experience.
I developed a proof of concept using the existing knowledge about the structure of murder
mysteries and procedural narrative generation in a way that it can provide information for a murder
mystery narrative-based game. After developing this proof of concept, I tested the prototype using a
human-computer interaction method called Wizard of Oz. I hypothesized that this prototype is able
to provide narrative information coherently for a compelling narrative experience with a believable
cast of characters that can evoke feelings of suspense and surprise. By surveying participants after
the play-tests, we concluded that the proof of concept can create a coherent narrative experience
with a believable cast of characters in a way that it sometimes can create the feeling of suspense for
players, but it was not able to create the feeling of surprise on the revelation of the culprit for most
users. Based on this study, the prototype was also able to create a generally compelling narrative
experience for the users.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Saffarizadeh, Arian
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:8 February 2022
Thesis Supervisor(s):Popa, Tiberiu and Lessard, Jonathan
Keywords:Procedural content generation, procedural narrative, murder mysteries, information games
ID Code:990294
Deposited By: Arian Saffarizadeh
Deposited On:16 Jun 2022 15:08
Last Modified:16 Jun 2022 15:08
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