Date of Award

5-2024

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Ben Samuel

Second Advisor

Abdullah Al Redwan Newaz

Third Advisor

Shreya Banerjee

Abstract

This thesis describes the design, implementation, and testing of a novel procedural narrative system called the Procedurally Adaptive Webbed Narrative (PAWN) system. PAWN procedurally generates characters and, responding to choices made by the player, produces more responsive characters and relationships involving the player and these narrative agents. Initially, this thesis discusses other interactive narrative types that exist, such as emergent or event-driven narratives, along with their strengths and weaknesses. It then examines each aspect of PAWN, starting with initial actor generation, then moving to the capturing of game events and translating them into logical objects called Occurrences. These Occurrences are then parsed into Predicates, which are more character-focused relational objects. These Predicates are continually queried for specific Narrative Patterns, and, if found, introduce more specific and interesting Predicates into the PAWN’s current Predicate list. Furthermore, the dialogue selection and tailoring process derived from the most interesting of these Predicates is explained. Conducting A/B tests with PAWN and the control narrative system (NONPAWN) in the same game base revealed unanimous user preference for PAWN, with users judging PAWN characters to be more responsive and interesting than their NONPAWN alternatives.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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