Available at: https://digitalcommons.calpoly.edu/theses/3068
Date of Award
6-2025
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
April Grow
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Games utilizing Procedural Level Generation (PLG), such as Roguelikes, are becoming increasingly popular in today's gaming sphere. In games employing PLG, levels are generated randomly or pseudo-randomly, and aim to retain player attention through variance in levels between playthroughs. However, when generating levels with variance in structure and design, player enjoyment is often a mixed bag. With low enjoyment, player retention for these games can dwindle. This study explores the efficacy of real-time difficulty adjustment in procedurally generated platformers, as a method for maintaining stable player enjoyment without causing frustration. This thesis focuses on creating a short user experience, MIMEVA, that aims to generate levels that match a player's skill level as they progress. A procedural level generator and level loader, PLGen, was created to assist in effeciently generating and loading in new levels into MIMEVA. Levels were generated during each playthrough according to each player's performance, with adjustments to the difficulty being calculated based on their performance in each prior level. A user study was conducted where participants were asked to play through several platformer levels generated in MIMEVA before answering questions regarding their experience. Data collected during the playtest and in the questionnaire was used in order to gauge the effectiveness of difficulty adjustments, and how they affected player enjoyment. The results of the study demonstrated accurate difficulty adjustments in players who were comfortable with platformers, but did not effectively create levels of the appropriate difficulty for players who were not as good.